Applied AI Scientist, GenAI and ML Prototyping

📍 Job Overview

Job Title: Applied AI Scientist, GenAI and ML Prototyping

Company: C2FO

Location: Noida, Uttar Pradesh, India (Remote)

Job Type: Full-time

Category: Artificial Intelligence / Machine Learning / Data Science

Date Posted: May 03, 2026

Experience Level: Mid-Senior Level (4-6 years)

Remote Status: Fully Remote

🚀 Role Summary

• Spearhead the discovery and rapid prototyping of cutting-edge AI solutions, focusing on Generative AI (GenAI) and Machine Learning (ML) applications.

• Bridge the gap between business needs and technical feasibility by identifying high-impact AI opportunities across internal operations and customer-facing products.

• Design and build functional Proofs of Concept (POCs) that clearly demonstrate measurable value, utilizing a range of AI approaches from rule-based systems to advanced LLM-driven agentic workflows.

• Act as a key technical liaison, translating complex AI concepts into business-understandable terms and managing stakeholder expectations throughout the discovery and prototyping phases.

• Facilitate seamless technology transfer by producing comprehensive documentation for the AI Engineering team, enabling the scalable production of validated prototypes.

📝 Enhancement Note: This role is positioned within the early stages of the AI development lifecycle, emphasizing innovation and rapid experimentation. The "Applied AI Scientist" title, coupled with "GenAI and ML Prototyping" and the focus on discovery and POCs, indicates a strong need for individuals who can not only build but also strategically identify and validate AI use cases. The emphasis on translating technical concepts for non-technical stakeholders is crucial for success.

📈 Primary Responsibilities

• Business Discovery & Opportunity Identification: Conduct structured discovery sessions with department heads and product owners to identify and scope potential AI applications that align with business objectives.

• Problem Framing & Scoping: Clearly define problem statements, assess data availability and constraints, and establish the foundational understanding necessary before commencing any prototyping activities.

• Rapid Prototyping & Solution Design: Develop functional POCs using appropriate AI methodologies, including Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, predictive ML models, and rule-based systems.

• Value Demonstration & Validation: Design and execute rigorous evaluations for prototypes, defining clear success criteria to ensure they are credible enough to support definitive build-or-not decisions.

• Stakeholder Engagement & Communication: Serve as the primary technical point of contact for business stakeholders, clearly articulating technical trade-offs (accuracy, cost, latency) and making evidence-based recommendations.

• Technical Handoff & Documentation: Produce detailed handoff documentation, including system design, prompt strategies, data requirements, known failure modes, and evaluation benchmarks, to facilitate the transition to the AI Engineering team.

• Technology Trend Monitoring: Stay abreast of the latest advancements in LLMs, GenAI, and ML techniques to inform prototyping strategies and identify emergent opportunities.

📝 Enhancement Note: The responsibilities highlight a dual focus on business acumen and technical execution. The role requires an individual who can navigate ambiguity, drive innovation, and effectively communicate technical value to a non-technical audience. The requirement for a "clean handoff" to engineering implies a need for well-documented and well-tested prototypes.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.

Experience:

• 4+ years of experience in data science, machine learning, or a closely related field, with a proven track record of delivering end-to-end projects.

• Minimum of 2 years of hands-on experience working with Large Language Models (LLMs) or Generative AI solutions in a professional capacity.

• Demonstrated experience in taking projects from initial business problem discovery through to a functional prototype or proof of concept.

• Proven ability to engage directly with non-technical business stakeholders to gather requirements, manage expectations, and clearly communicate project results.

Required Skills:

• Programming Proficiency: Expertise in Python for data science and ML development, including libraries like Pandas, NumPy, and Scikit-learn.

• Data Querying: Strong command of SQL for data extraction and manipulation from modern data warehouses (e.g., BigQuery, Snowflake, PostgreSQL).

• LLM API Interaction: Hands-on experience with LLM APIs from providers such as OpenAI (GPT-4o), Anthropic (Claude), or Google (Gemini).

• Prompt Engineering: Mastery of prompt engineering techniques, including few-shot prompting, chain-of-thought reasoning, and structured output design.

• RAG Pipelines: Practical experience building Retrieval-Augmented Generation (RAG) pipelines, encompassing chunking strategies, embedding models, and retrieval tuning.

• Agentic Orchestration: Familiarity with agentic orchestration frameworks like LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.

• Stakeholder Management: Excellent ability to manage relationships with non-technical business stakeholders, setting realistic expectations and communicating complex technical information clearly.

• Problem Solving & Analysis: Structured thinking to define evaluation criteria and establish clear success metrics for prototypes.

Preferred Skills:

• Deep Learning Frameworks: Working knowledge of deep learning frameworks such as PyTorch or TensorFlow for model experimentation.

• Open-Source LLMs: Experience with open-source LLMs (e.g., Mistral, LLaMA) and understanding their application relative to proprietary models.

• Vector Databases: Experience integrating vector databases (e.g., pgvector, Pinecone, Weaviate, ChromaDB) into search and retrieval workflows.

• Tool/Function Calling: Understanding of tool/function calling patterns for LLM-driven automation.

• LLM Evaluation: Experience with LLM evaluation libraries such as RAGAS, TruLens, or DeepEval.

• Experiment Tracking: Familiarity with experiment tracking tools like MLflow or Weights & Biases.

• Cloud Environments: Comfort working within cloud platforms (AWS, GCP, Azure) for data access, compute, and API integration.

• Docker: Basic familiarity with Docker for packaging and sharing POC environments.

• Fine-Tuning LLMs: Experience with fine-tuning or instruction-tuning LLMs on domain-specific datasets.

• Responsible AI: Familiarity with responsible AI principles, including bias detection, fairness evaluation, and model transparency.

• Business Process Mapping: Knowledge of business process mapping techniques (e.g., BPMN) to support discovery sessions.

• Open Source Contributions: Public work demonstrating applied AI expertise (e.g., GitHub contributions, research papers).

📝 Enhancement Note: The qualifications emphasize a blend of deep technical expertise in AI/ML and GenAI, coupled with strong communication and stakeholder management skills. The requirement for both traditional ML and modern LLM experience suggests a need for a versatile data scientist. The "Nice to Have" section indicates areas where candidates can further differentiate themselves.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies of Impact: Showcase at least 2-3 detailed case studies demonstrating how you've translated business problems into AI/ML solutions, with a particular focus on GenAI/LLM applications.

• POC Demonstrations: Include examples of prototypes or proofs of concept you have built, clearly outlining the problem, approach, technology stack, and the validated results or insights gained.

• Data & Model Artifacts: Where possible, provide anonymized snippets of code, data schemas, or model architecture diagrams that illustrate your technical approach and problem-solving methodology.

• Evaluation Frameworks: Demonstrate your ability to define and implement relevant metrics and evaluation frameworks for AI/ML models and GenAI applications.

• Business Value Articulation: For each project in your portfolio, clearly articulate the business problem addressed and the quantifiable or qualitative value delivered by your solution.

Process Documentation:

• Discovery & Scoping Documentation: Provide examples of how you document problem statements, data requirements, and initial scoping for AI projects.

• Prototype Design & Architecture: Showcase documentation outlining the design and architecture of your prototypes, including prompt strategies, RAG pipeline components, and integration points.

• Evaluation Reports: Include examples of evaluation reports that detail the methodology, results, and conclusions drawn from testing AI/ML prototypes.

• Technical Handoff Artifacts: Prepare a sample of the type of documentation you would provide for a technical handoff to an engineering team, covering system design, known limitations, and operational considerations.

📝 Enhancement Note: A strong portfolio is critical for this role, as it directly assesses a candidate's ability to execute on the core responsibilities. Candidates should prepare to walk through their projects, highlighting their decision-making process, technical choices, and the impact of their work. The emphasis on "rapid prototyping" and "POCs" suggests that showcasing early-stage, impactful work is highly valued.

💵 Compensation & Benefits

Salary Range:

Given the location (Noida, India) and the experience level (4-6 years with specialized GenAI/ML skills), the estimated annual salary range for an Applied AI Scientist with this profile in India would typically fall between ₹15,00,000 and ₹28,00,000 (Indian Rupees). This range can vary based on the candidate's specific skills, the depth of their experience with LLMs and GenAI, and C2FO's compensation philosophy.

📝 Enhancement Note: This salary estimate is based on market research for experienced AI/ML Scientists in India, with a premium for specialized GenAI and LLM expertise. Factors such as the candidate's negotiation skills, the specific demands of the role, and C2FO's internal compensation bands will influence the final offer. The role being remote within India may also affect local compensation benchmarks.

Benefits:

• Comprehensive Benefits Package: C2FO offers a robust benefits package designed to support employee well-being and financial security.

• Flexible Work Options: Embracing a remote-first or hybrid approach, C2FO provides flexibility to support work-life balance, allowing employees to work from locations that best suit their productivity.

• Volunteer Time Off (VTO): Encouragement and paid time off for employees to contribute to their communities and support causes they care about.

• Employee Ownership: As an employee-owned company, team members are invested in C2FO's success, fostering a strong sense of shared purpose and reward.

• Learning and Development Opportunities: Access to resources and support for continuous learning and professional growth, crucial in the rapidly evolving AI field.

Working Hours:

• Standard full-time work hours, typically around 40 hours per week.

• Given the remote nature and the dynamic field of AI, flexibility may be expected to meet project deadlines and collaborate across different time zones, though core hours will likely be aligned with Indian business days.

📝 Enhancement Note: The benefits highlight C2FO's commitment to employee well-being and professional development, with a unique aspect of employee ownership. The flexible work options are particularly relevant for a remote role in the AI/ML space, where focused work is often paramount.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Technology (FinTech), specifically focused on working capital solutions. C2FO operates at the intersection of finance and technology, leveraging its platform to provide businesses with essential capital.

Company Size: Over 500 employees worldwide. This indicates a mid-to-large size organization that can offer both established processes and opportunities for impact.

Founded: C2FO was founded to create a better financial system, providing businesses with access to working capital. Its mission is centered on enabling business growth and job creation through its on-demand capital platform.

Team Structure:

• AI/Data Science Team: This role likely sits within or closely collaborates with a dedicated AI or Data Science team, responsible for driving innovation and implementing advanced analytics and AI solutions.

• Cross-Functional Collaboration: Expect close partnerships with department heads (e.g., Finance, Product, Operations) and product owners to identify and scope AI opportunities.

• AI Engineering Partnership: A crucial relationship will be with the AI Engineering team, responsible for productionizing validated prototypes. This requires clear communication and documentation for a smooth handoff.

• Reporting Structure: The Applied AI Scientist will likely report to a Lead Data Scientist, Head of AI, or a similar technical leadership role.

Methodology:

• Data-Driven Decision Making: C2FO emphasizes using data to inform decisions, particularly in validating the potential of AI solutions through prototypes and POCs.

• Agile Prototyping: The role requires a rapid, iterative approach to building and testing prototypes, prioritizing speed and learning in an ambiguous environment.

• Customer-Centric Innovation: AI initiatives are aimed at improving both internal operations and customer-facing products, reflecting a focus on delivering tangible value to users.

• Collaborative Problem Solving: The emphasis on working with department heads and product owners underscores a collaborative approach to identifying and solving business challenges with AI.

Company Website: https://www.c2fo.com/

📝 Enhancement Note: C2FO's FinTech focus means that AI solutions will likely be applied to financial processes, risk assessment, customer interactions, and operational efficiency within the working capital domain. The "employee-owner" culture suggests a highly engaged and collaborative workforce.

📈 Career & Growth Analysis

Operations Career Level: This is a mid-to-senior level individual contributor role, focused on specialized technical expertise in Applied AI, GenAI, and ML prototyping. It sits at the critical intersection of business strategy and technical execution within the AI lifecycle.

Reporting Structure: The role typically reports to a senior technical leader within the AI/Data Science function, such as a Principal Data Scientist, Head of AI, or Director of Data Science. Close collaboration with product management and business unit leaders is expected.

Operations Impact: The direct impact of this role is on driving innovation and identifying new avenues for AI application that can significantly enhance C2FO's platform, internal efficiencies, and customer value. Successful prototypes can lead to new product features, improved operational workflows, and substantial business value.

Growth Opportunities:

• Technical Specialization: Deepen expertise in cutting-edge GenAI techniques, LLM orchestration, and advanced ML modeling, potentially becoming a subject matter expert within C2FO.

• Leadership in AI Incubation: Grow into a role leading the AI discovery and prototyping function, mentoring junior scientists, and shaping the company's AI strategy.

• Transition to AI Engineering/Product: Gain deep understanding of production systems, potentially leading to opportunities in AI Engineering, ML Operations (MLOps), or AI Product Management.

• Cross-Functional Influence: Develop strong business acumen and communication skills, enabling influence across various departments and strategic decision-making processes.

• Industry Recognition: Contribute to open-source projects or research that could lead to industry recognition and thought leadership opportunities.

📝 Enhancement Note: This role offers a unique opportunity to be at the forefront of AI adoption within a FinTech company. Growth pathways are strong for individuals who can combine technical prowess with strategic business understanding and effective communication. The emphasis on prototyping and POCs means success is measured by the ability to innovate and validate new AI applications.

🌐 Work Environment

Office Type: Fully Remote. This role operates without a physical office requirement, allowing for flexibility in work location within India.

Office Location(s): While the role is remote, the company has a significant presence in Noida, UP, India, with global headquarters in Kansas City, USA. Remote employees are typically expected to align with business hours of their primary region.

Workspace Context:

• Autonomous Work: Expect a high degree of autonomy and self-direction, common in remote, R&D-focused roles.

• Digital Collaboration Tools: Reliance on digital collaboration tools (e.g., Slack, Microsoft Teams, Zoom, Jira) for communication, project management, and knowledge sharing.

• Access to Cloud Resources: Will require access to cloud computing resources (AWS, GCP, Azure) for data processing, model training, and deployment of prototypes.

• Focus on Output: The environment is likely results-oriented, with emphasis on delivering functional prototypes and clear insights rather than strict adherence to traditional office structures.

Work Schedule:

• Standard full-time hours (approx. 40 hours/week) are expected.

• Flexibility may be required to accommodate collaboration with teams or stakeholders in different time zones, particularly during critical phases of discovery or prototyping.

• Emphasis on asynchronous communication where possible, but real-time collaboration will be necessary for specific meetings and discussions.

📝 Enhancement Note: The remote nature of the role necessitates strong self-discipline, effective time management, and proactive communication. Candidates should be comfortable working independently and leveraging digital tools to stay connected and productive.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A call with a recruiter to assess general fit, experience, and interest in the role and C2FO.

• Technical Screening: An interview with a Data Scientist or AI Lead focusing on core technical skills, ML/GenAI knowledge, and problem-solving abilities. This may include coding exercises or theoretical questions.

• Deep Dive / Case Study Interview: A more in-depth session, potentially involving a take-home assignment or a live coding/design challenge. This will focus on your ability to frame problems, design solutions, and articulate technical trade-offs. This is where your portfolio will be heavily reviewed.

• Hiring Manager Interview: A discussion with the hiring manager to assess leadership potential, strategic thinking, stakeholder management skills, and cultural fit.

• Cross-Functional / Stakeholder Interview: An interview with potential collaborators (e.g., Product Manager, Business Unit Lead) to evaluate your ability to communicate with non-technical audiences and understand business needs.

• Final Round / Offer: Potential final interview with senior leadership, followed by an offer.

Portfolio Review Tips:

• Structure Your Narrative: For each portfolio piece, clearly define the business problem, your role, the technical approach (including why you chose specific tools/models), the challenges faced, and the quantifiable results or key learnings.

• Highlight GenAI/LLM Expertise: Emphasize projects involving LLMs, RAG, agentic workflows, and prompt engineering. Showcase your understanding of their capabilities and limitations.

• Demonstrate POC Value: For prototypes, clearly articulate how they validated a hypothesis or provided actionable insights that led to a go/no-go decision.

• Code & Technical Depth: Be prepared to discuss your code, architectural decisions, and the rationale behind your technical choices.

• Business Acumen: Connect your technical work back to business impact. Show that you understand how AI drives value for the organization.

• Conciseness: Be prepared to present your portfolio effectively within allocated time, focusing on the most impactful projects.

Challenge Preparation:

• Problem Framing: Practice breaking down ambiguous business problems into clear, solvable technical challenges.

• Technical Design: Be ready to design AI/ML solutions, including data pipelines, model architectures, evaluation strategies, and potential deployment considerations.

• LLM Specifics: Prepare to discuss prompt design, RAG implementation, agentic system design, and cost/latency considerations for LLM applications.

• Communication: Practice explaining complex technical concepts to non-technical stakeholders. Focus on clarity, impact, and managing expectations.

• Evaluation Metrics: Be ready to define appropriate metrics for various AI/ML tasks and GenAI applications.

📝 Enhancement Note: The interview process is designed to assess both deep technical skills and the ability to translate those skills into business impact. A strong, well-documented portfolio that clearly demonstrates GenAI/LLM prototyping experience and stakeholder management is essential. Candidates should be ready to discuss their decision-making process and defend their technical choices.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), SQL.

• Core ML Libraries: Pandas, NumPy, Scikit-learn.

• Deep Learning Frameworks: PyTorch, TensorFlow (preferred).

• Generative AI & LLM Tooling:

  • LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini).
  • Orchestration Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen.
  • Vector Databases: pgvector, Pinecone, Weaviate, ChromaDB.
  • LLM Evaluation Libraries: RAGAS, TruLens, DeepEval.

• Development Tools: Git, Jupyter Notebooks, VS Code.

• Cloud Platforms: AWS, GCP, or Azure (familiarity required).

• Containerization: Docker (basic familiarity).

• Experiment Tracking: MLflow, Weights & Biases (familiarity is a plus).

Analytics & Reporting:

CRM & Automation:

• Not directly specified for this role, as the focus is on prototyping rather than production system implementation or CRM management. However, understanding how AI prototypes might integrate with existing CRM or automation systems could be a plus.

📝 Enhancement Note: This role requires a hands-on understanding of a modern GenAI technology stack. Proficiency with Python and SQL is foundational, while deep experience with LLM APIs, orchestration frameworks, vector databases, and prompt engineering is critical. Familiarity with cloud environments and standard development tools is also expected.

👥 Team Culture & Values

Operations Values:

• Innovation & Experimentation: C2FO fosters a culture that encourages exploring new technologies and rapid prototyping to drive innovation, especially in the AI space.

• Data-Driven Approach: Decisions, particularly regarding the viability of AI solutions, are based on evidence derived from rigorous evaluation and prototyping.

• Collaboration & Ownership: As an employee-owned company, there's a strong emphasis on teamwork, shared responsibility, and collective success.

• Efficiency & Impact: The goal is to leverage AI to create tangible value, improve efficiency, and drive business growth.

• Transparency & Communication: Open communication is key, particularly in translating complex technical concepts for business stakeholders and ensuring clear handoffs.

Collaboration Style:

• Cross-Functional Integration: Expect to work closely with business leaders, product managers, and engineers, requiring strong interpersonal and communication skills.

• Prototyping-Centric: Collaboration will revolve around the rapid development, testing, and iteration of AI prototypes.

• Knowledge Sharing: A culture that encourages sharing insights, best practices, and learnings from experiments within the AI and broader technical teams.

• Feedback Loops: Openness to receiving and providing constructive feedback to ensure continuous improvement of prototypes and processes.

📝 Enhancement Note: The company culture emphasizes innovation, collaboration, and data-driven decision-making, aligning well with the exploratory nature of this AI Scientist role. The employee-owner model likely fosters a high level of engagement and commitment.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity: The role involves working in an early-stage discovery and prototyping environment, which inherently comes with ambiguity. Candidates must be comfortable defining problems and solutions from scratch.

• Rapid Technological Evolution: The GenAI and LLM landscape is evolving at an unprecedented pace. Staying current and adapting quickly to new tools and techniques is a continuous challenge.

• Balancing Speed and Rigor: The need for "rapid prototyping" must be balanced with the requirement for rigorous evaluation and validation to ensure that prototypes truly demonstrate value and are not just "concepts."

• Communicating Limitations: Effectively managing stakeholder expectations around the current capabilities and limitations of LLMs, especially when they are still experimental, can be challenging.

• Handoff to Production: Ensuring a smooth and effective transition of prototypes to the AI Engineering team requires meticulous documentation and clear communication of complex technical details.

Learning & Development Opportunities:

• Cutting-Edge AI Exposure: Direct involvement with the latest GenAI and LLM technologies, providing unparalleled learning opportunities.

• Cross-Functional Skill Development: Enhancing business acumen, stakeholder management, and strategic thinking by working closely with various departments.

• Specialized Training: Opportunities for training in advanced AI techniques, specific LLM frameworks, or relevant cloud technologies.

• Mentorship: Potential to learn from senior AI leaders and engineers within C2FO.

• Industry Conferences & Resources: Access to resources and potential attendance at industry events focused on AI, ML, and GenAI.

📝 Enhancement Note: This role is ideal for individuals who thrive on solving complex, open-ended problems and are excited by the rapid advancements in AI. The challenges presented are opportunities for significant professional growth and skill development.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to identify an AI opportunity from scratch within a business context. How did you frame the problem and what was the outcome?" (Prepare a case study from your portfolio focusing on discovery).

• "How do you balance the need for rapid prototyping with the requirement for rigorous evaluation in an LLM project?" (Discuss your approach to defining metrics and validating POCs).

Company & Culture Questions:

• "What interests you most about C2FO and our mission to provide working capital?" (Research C2FO's business model and impact).

• "How do you approach working in a remote, collaborative environment, especially when dealing with complex technical projects?" (Discuss your communication strategies and tools).

Portfolio Presentation Strategy:

• Select High-Impact Projects: Choose 2-3 projects that best showcase your GenAI/LLM prototyping, problem-solving, and stakeholder communication skills.

• Structure Your Narrative: For each project: Problem -> Your Role -> Technical Approach (including rationale for tools/models) -> Key Challenges -> Results/Learnings -> Business Impact.

• Be Ready for Deep Dives: Anticipate questions about your code, architectural decisions, evaluation methodologies, and the "why" behind your choices.

• Focus on POC Value: Clearly articulate how your prototypes provided actionable insights or validated a hypothesis, leading to a clear decision.

• Show, Don't Just Tell: If possible, prepare a brief live demo or walkthrough of a prototype or relevant code snippets.

📝 Enhancement Note: Be prepared to demonstrate your ability to not only build AI solutions but also to strategically identify opportunities, communicate technical concepts effectively, and manage the entire lifecycle from discovery to a validated prototype. Your portfolio is your strongest asset here.

📌 Application Steps

To apply for this Applied AI Scientist position:

• Submit Your Application: Complete the online application form through the provided link.

• Tailor Your Resume: Highlight your experience with Generative AI, Large Language Models, rapid prototyping, and stakeholder management. Use keywords from the job description.

• Prepare Your Portfolio: Curate 2-3 strong case studies or project examples that showcase your relevant skills, particularly in GenAI/LLM prototyping and problem-solving. Ensure you can clearly articulate the business problem, your approach, and the impact.

• Research C2FO: Understand C2FO's business model, mission, and recent developments. This will help you tailor your application and prepare for interview questions.

• Practice Your Narrative: Rehearse presenting your portfolio projects and answering common interview questions, focusing on clarity, impact, and technical depth.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.