Senior AI Engineer (Rapid Prototyping & Analytics )

📍 Job Overview

Job Title: Senior AI Engineer (Rapid Prototyping & Analytics)

Company: Prompt

Location: United States

Job Type: Full-Time

Category: Artificial Intelligence / Machine Learning Operations

Date Posted: 2026-01-05

Experience Level: 5-10 years

Remote Status: Fully Remote

🚀 Role Summary

• Spearhead the development and deployment of end-to-end AI and ML systems, with a strong emphasis on speech and language processing within the healthcare tech sector.

• Drive the rapid prototyping and iterative development of AI solutions, ensuring their seamless integration into both product offerings and internal operational workflows.

• Define, implement, and monitor critical evaluation and observability metrics, encompassing accuracy, cost, latency, and direct business impact to ensure AI initiatives deliver tangible value.

• Foster strong cross-functional collaboration with stakeholders across Engineering, Design, Sales, Marketing, Support, Finance, and Operations to translate complex workflows into effective and adopted AI solutions.

• Contribute to the growth of the AI team by mentoring junior engineers and promoting best practices in AI system development and deployment.

📝 Enhancement Note: This role is positioned as a Senior AI Engineer with a strong emphasis on "Rapid Prototyping & Analytics." While the core responsibilities lean heavily into AI/ML engineering and system development, the "Analytics" aspect and the focus on internal tools suggest a significant overlap with operations, particularly in areas like performance monitoring, ROI tracking, and efficiency improvements. This role will likely require a deep understanding of how AI solutions impact business operations and how to measure that impact effectively.

📈 Primary Responsibilities

• Lead AI/ML projects from initial ideation and architectural design through to production deployment and ongoing iteration, with a focus on driving user adoption and satisfaction.

• Design, build, and deploy robust end-to-end AI systems, integrating both traditional machine learning models and advanced LLM-based workflows.

• Partner closely with internal stakeholders to thoroughly understand their current workflows, collaboratively define success criteria, and deliver practical, impactful AI-driven solutions.

• Contribute to both product-facing AI initiatives and the development of internal AI tools designed to enhance company-wide efficiency, decision-making, and quality across all departments.

• Develop and implement comprehensive evaluation, monitoring, and observability metrics for AI systems, tracking key performance indicators such as accuracy, cost, latency, and overall business impact.

• Implement robust guardrails for LLM pipelines, including rigorous validation processes and effective human-in-the-loop workflows to ensure reliability and safety.

• Build rapid proofs-of-concept and prototypes to validate AI approaches, then systematically harden these into stable, production-ready systems.

• Guide and mentor junior AI team members, contributing to their technical growth and fostering a culture of continuous learning and high performance.

📝 Enhancement Note: The description emphasizes "end-to-end AI systems" and "monitoring, observability metrics (accuracy, cost, latency, and business impact)," which aligns with operations principles of system oversight, performance tracking, and value realization. The responsibility to "design and implement evaluation, monitoring, and observability metrics" directly relates to data analysis and performance management crucial in operations roles.

🎓 Skills & Qualifications

Education: While no specific degree is mandated, a strong academic background in Computer Science, Engineering, Data Science, or a related quantitative field is implied by the technical requirements and experience expectations.

Experience: 4+ years of hands-on experience building AI/ML systems in industry or academia, with a proven track record of significant production ownership and end-to-end system development.

Required Skills:

• Extensive experience building and operating AI/ML systems end-to-end, moving beyond isolated model development to full system integration and lifecycle management.

• Strong proficiency with LLM-based systems, including programmatic prompt optimization, evaluation techniques, structured output generation, Retrieval Augmented Generation (RAG), and multi-step workflow design.

• Solid understanding of AI system evaluation methodologies, including offline metrics, online monitoring strategies, regression testing, and cost tracking.

• Demonstrated ability to collaborate effectively with non-technical stakeholders, translating complex business workflows into practical and successful AI solutions.

• Strong software engineering fundamentals, with a proven ability to ship maintainable, production-grade systems that are scalable and reliable.

• Track record of benchmarking AI systems and iteratively improving key performance indicators such as accuracy, cost, and latency.

• Excellent communication skills, with the capacity to articulate complex AI concepts clearly and concisely to both technical and non-technical audiences.

• Proficiency in Python (version 3.9 or higher).

• Basic understanding of Docker for containerization.

• Strong problem-solving mindset, with the ability to operate effectively and deliver results in ambiguous, fast-paced environments.

Preferred Skills:

• Experience building internal AI tools or platforms that are utilized across multiple business functions, demonstrating an understanding of operational efficiency drivers.

• Experience designing comprehensive evaluation harnesses for LLM workflows, including the creation of golden datasets, regression tests, and human review loops.

• Proven experience setting up analytics and dashboards to effectively track AI usage, measure outcomes, and demonstrate Return on Investment (ROI).

• Familiarity with workflow automation tools and core business systems such as Customer Relationship Management (CRM), support platforms, and analytics suites.

• Experience with FastAPI, Streamlit, Pydantic, SQLAlchemy, and PostgreSQL.

• Experience with retrieval systems, including vector search, hybrid search, and grounding/citation strategies.

• Prior experience building healthcare software or developing data processing pipelines within the healthcare domain.

📝 Enhancement Note: The requirement for "4+ years of experience" and the emphasis on "production ownership" and "end-to-end AI systems" indicate a role that expects significant practical application and a mature understanding of system lifecycles, aligning with the expectations for a senior operations-focused engineering role. The preference for experience with internal AI tools and analytics/dashboards strongly suggests a focus on operational impact and measurement.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of end-to-end AI/ML system development, showcasing the entire lifecycle from problem definition to production deployment and monitoring.

• Case studies detailing the design and implementation of LLM-based workflows, highlighting specific challenges addressed and solutions developed (e.g., prompt engineering, RAG implementation, evaluation strategies).

• Evidence of work on rapid prototyping, illustrating the ability to quickly iterate on AI concepts and deliver functional proofs-of-concept.

Process Documentation:

• Documentation of AI system architectures, including diagrams and explanations of how different components (models, data pipelines, APIs, monitoring tools) integrate and function.

• Detailed write-ups of evaluation methodologies used for AI models and systems, including the rationale behind metric selection, data preparation for evaluation, and interpretation of results.

• Examples of process improvements driven by AI, potentially including workflow automation, enhanced decision-making processes, or efficiency gains in operational tasks.

• Records of system monitoring and observability practices, detailing how AI system performance, health, and business impact are tracked and reported.

📝 Enhancement Note: While not explicitly stated as "portfolio requirements," the job description heavily implies the need to demonstrate these capabilities. The emphasis on "end-to-end AI systems," "evaluation, monitoring, and observability metrics," and "rapid prototypes" suggests that candidates will need to present tangible evidence of their work in these areas, akin to a process portfolio for operations roles.

💵 Compensation & Benefits

Salary Range: $160,000 - $220,000 USD per year.

Benefits:

• Competitive salaries.

• Potential equity compensation for outstanding performance.

• Flexible Paid Time Off (PTO).

• Company-wide sponsored lunches.

• Company-paid disability and life insurance benefits.

• Company-paid family and medical leave.

• Comprehensive medical, dental, and vision insurance benefits.

• Discounted pet insurance.

• Flexible Spending Account (FSA)/Dependent Care Account (DCA) and commuter benefits.

• 401k retirement savings plan.

• Credits for online fitness classes/gym memberships.

• Access to a recovery suite at HQ (includes cold plunge, sauna, and shower) - Note: This benefit may be location-dependent if HQ access is required.

Working Hours: Standard full-time hours are expected, likely around 40 hours per week, with flexibility for project-based work and remote collaboration.

📝 Enhancement Note: The provided salary range ($160,000 - $220,000 USD) is typical for a Senior AI Engineer role in the US, especially within a fast-paced startup environment. The extensive benefits package is comprehensive and competitive, offering a strong mix of health, wellness, financial, and work-life balance perks, which are attractive to experienced professionals in technology and operations roles. The mention of a recovery suite at HQ suggests a hybrid or in-office component might exist for some employees, even with a remote-first designation.

🎯 Team & Company Context

🏢 Company Culture

Industry: Healthcare Technology (specifically B2B enterprise software for rehab therapy businesses).

Company Size: Prompt is described as a "mission-driven startup" that is "rapidly growing." This suggests a dynamic, agile environment with a relatively smaller, but expanding team.

Founded: The founding date is not specified, but its current stage implies it has moved beyond the initial seed stage and is focused on scaling its product and market share.

Team Structure:

• The AI team is likely to be a growing, specialized unit within the broader engineering organization.

• This Senior AI Engineer role will report into an AI Lead or Head of Engineering, with potential for future leadership opportunities as the team expands.

Methodology:

• Emphasis on iterative development and rapid prototyping to deliver value quickly.

• Data-driven decision-making, with a strong focus on metrics, evaluation, and monitoring of AI systems.

• A culture that prioritizes building "software people love" and improving healthcare through modern technology.

• Focus on building internal AI tools to enhance efficiency and decision-making across the entire organization, indicating a strong operational mindset.

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

📝 Enhancement Note: The company's focus on B2B enterprise software in healthcare, coupled with its rapid growth and emphasis on AI, suggests a culture that values innovation, efficiency, and tangible business impact. The role's responsibility for internal AI tools further underscores an operations-centric approach to leveraging technology for organizational improvement.

📈 Career & Growth Analysis

Operations Career Level: This role is explicitly a "Senior AI Engineer" position, indicating a level of technical expertise and autonomy beyond junior or mid-level roles. The emphasis is on individual contribution and technical leadership within AI projects, rather than management.

Reporting Structure: The Senior AI Engineer will likely report to an AI/ML Manager or Director, or potentially the VP of Engineering. They will work closely with product managers and stakeholders from various departments to define project requirements and deliver solutions.

Operations Impact: The AI Engineer will have a significant impact on both product development and internal operations. By improving product offerings with AI features and by creating internal tools that enhance efficiency, decision-making, and quality across departments like Sales, Marketing, Support, Finance, and Operations, this role directly contributes to the company's scalability and effectiveness.

Growth Opportunities:

• Technical Specialization: Deepen expertise in cutting-edge AI/ML technologies, particularly LLMs, speech/language processing, and AI system evaluation.

• Leadership Track: As the AI team grows, opportunities may arise to lead projects, mentor junior engineers, and potentially move into an AI Team Lead or management role.

• Cross-Functional Influence: Develop a strong understanding of various business functions and how AI can be leveraged to solve their unique operational challenges, becoming a key strategic partner.

• Domain Expertise: Gain in-depth knowledge of the healthcare and physical therapy sector, enabling more impactful AI solutions tailored to specific industry needs.

• Architecture & System Design: Grow responsibilities in designing and architecting complex, scalable AI systems from the ground up.

📝 Enhancement Note: The "Senior" title and the mention of "helping grow and lead the AI team" suggest a clear path for advancement. The explicit statement that "this is not an engineering management position, though growth opportunities exist over time" clarifies the immediate focus on hands-on technical contribution with potential for future leadership. The emphasis on internal AI tools also positions this role as having a significant operational impact, which can be a strong career differentiator.

🌐 Work Environment

Office Type: The company offers a "Remote/hybrid environment," with a specific mention of a "Recovery suite at HQ." This implies that while the role is fully remote, there might be an office location for those who prefer hybrid work or for specific team gatherings.

Office Location(s): While the job is fully remote within the USA, the mention of an "HQ" suggests a physical headquarters exists, though its specific location isn't provided. This could be relevant for occasional team events or access to specific facilities.

Workspace Context:

• A fully remote setup allows for flexibility and autonomy in managing one's work environment.

• The emphasis on building "software people love" and delivering "practical solutions" suggests a collaborative and results-oriented environment, even when remote.

• Access to advanced AI tools and technologies will be central to the daily work.

• Opportunities for interaction with a diverse set of stakeholders across the company will be frequent, fostering a dynamic and engaging remote work experience.

Work Schedule: While the role is full-time, the remote nature and startup environment likely offer a degree of flexibility in working hours, provided that project deadlines are met and collaboration with team members across different time zones is managed effectively. The focus is on output and impact rather than strict adherence to a 9-to-5 schedule.

📝 Enhancement Note: The "Fully Remote" status for the USA is a key aspect. The mention of an HQ and specific amenities there (like the recovery suite) is more of a perk for those who might visit or be located near HQ, rather than a requirement for remote employees. The environment is characterized by high ownership, rapid iteration, and collaboration, typical of a growing tech startup.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of applications and resumes to assess experience, technical skills, and alignment with the role's core requirements (AI/ML systems, LLMs, Python, production ownership).

• Technical Interview(s): In-depth discussions focusing on AI/ML concepts, LLM architectures, system design, evaluation methodologies, and coding proficiency (likely involving live coding or code review exercises).

• System Design/Prototyping Challenge: A practical exercise where candidates might be asked to design an AI system for a specific use case or build a small prototype to demonstrate their approach to rapid development and problem-solving. This will heavily involve assessing their approach to analytics and metrics.

• Stakeholder/Behavioral Interview: Conversations with hiring managers and potentially cross-functional stakeholders to assess communication skills, problem-solving approach, cultural fit, and ability to translate technical solutions into business value.

• Final Round: May involve a discussion with senior leadership to confirm alignment on vision and strategic impact.

Portfolio Review Tips:

• Showcase End-to-End Projects: Highlight projects where you were involved in the entire lifecycle of an AI/ML system, from conception to deployment and monitoring. Clearly articulate your specific contributions.

• Emphasize LLM Experience: Provide concrete examples of your work with LLMs, detailing the specific techniques used (prompting, RAG, evaluation) and the outcomes achieved.

• Quantify Impact: For each project, clearly present the metrics you used for evaluation and the impact your work had (e.g., accuracy improvements, cost reductions, latency decreases, business value generated). This is crucial for the "Analytics" aspect of the role.

• Demonstrate Prototyping Skills: Include examples of rapid prototypes or proofs-of-concept you've developed, explaining the process and how they led to further development or informed decisions.

• Tailor to Prompt's Context: If possible, frame your experience in a way that highlights relevance to healthcare tech or B2B enterprise software, and demonstrate an understanding of how AI can drive operational efficiency.

Challenge Preparation:

• System Design: Be prepared to design an end-to-end AI system, considering components, data flow, scalability, reliability, and monitoring. Think about how you would integrate LLMs and traditional ML.

• Coding: Brush up on Python fundamentals, data structures, algorithms, and common ML libraries. Be ready for practical coding challenges.

• Metrics & Evaluation: Practice articulating how you would define success for an AI project, select appropriate metrics, and set up monitoring for performance and business impact.

• Stakeholder Communication: Prepare examples of how you've worked with non-technical stakeholders, translated requirements, and communicated complex technical concepts.

📝 Enhancement Note: The emphasis on "Rapid Prototyping & Analytics" means interviewers will be looking for candidates who can not only build AI systems but also rigorously measure their effectiveness and impact. Portfolio examples should strongly feature quantitative results and clear demonstrations of analytical thinking.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Language: Python (versions 3.9+ required).

• Containerization: Docker (basic familiarity required).

• Cloud Platform: AWS (familiarity preferred), specifically services like S3, RDS, EC2.

• LLM Frameworks/Libraries: Experience with common LLM interaction patterns, prompt engineering tools, and potentially libraries for building LLM applications.

• Web Frameworks (Preferred): FastAPI for building APIs, Streamlit for rapid UI prototyping.

• Data Handling (Preferred): Pydantic for data validation, SQLAlchemy for ORM, PostgreSQL for database management.

• Retrieval Systems (Preferred): Knowledge of vector databases and search technologies.

Analytics & Reporting:

• Metrics Definition & Tracking: Tools and methodologies to define, implement, and monitor AI system performance (accuracy, latency, cost) and business impact. This could involve custom dashboards or leveraging existing analytics platforms.

• Observability Tools: Systems for monitoring application health, performance, and user behavior in production.

• Data Visualization: Potentially tools for creating dashboards to communicate AI performance and ROI to stakeholders.

CRM & Automation:

• Workflow Automation (Preferred): Familiarity with how AI can integrate into and automate business workflows.

• Business Systems (Preferred): Understanding of how AI tools interact with CRMs, support tools, and analytics platforms.

📝 Enhancement Note: The list of required and preferred tools highlights a modern tech stack focused on Python, cloud services, and LLM development. The emphasis on FastAPI, Streamlit, and Pydantic points towards a preference for efficient, API-driven development and rapid application building. The inclusion of SQL/PostgreSQL indicates a need for robust data management.

👥 Team Culture & Values

Operations Values:

• Impact-Driven: A strong focus on delivering tangible business value and measurable improvements through AI solutions, whether for products or internal operations.

• Data-Centricity: Decisions and evaluations are heavily guided by data, metrics, and robust analytical approaches.

• Iterative Innovation: A culture that embraces rapid prototyping, continuous learning, and iterative refinement of AI systems.

• Collaboration & Ownership: High degree of individual ownership over projects, coupled with strong collaboration across teams to ensure solutions meet diverse needs.

• Efficiency & Automation: A drive to leverage AI to streamline processes, reduce costs, and improve overall organizational efficiency.

Collaboration Style:

• Cross-Functional Integration: Close partnerships with product, engineering, design, sales, marketing, support, finance, and operations teams to ensure AI solutions are aligned with business goals and user needs.

• Proactive Communication: Open and transparent communication regarding project status, challenges, and outcomes, particularly with non-technical stakeholders.

• Feedback Loops: An environment where feedback is actively sought and incorporated into AI development and system improvements.

• Knowledge Sharing: Encouragement of sharing insights, best practices, and learnings within the AI team and across the wider engineering organization.

📝 Enhancement Note: The company's values, as inferred from the job description, strongly align with operational excellence. The emphasis on "delivering real value," "improving efficiency, decision-making, and quality," and "designing, evaluating, and monitoring end-to-end AI systems" all point to a culture that appreciates and rewards operational impact.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapid Iteration in a Dynamic Environment: Balancing the need for rapid prototyping and deployment with the requirement for robust, production-grade systems and rigorous evaluation.

• Translating Complex Workflows: Effectively understanding and converting intricate healthcare and business processes into AI-solvable problems requires deep domain understanding and strong analytical skills.

• Measuring ROI for AI: Quantifying the precise business impact and ROI of internal AI tools can be challenging but is a critical aspect of this role.

• Healthcare Data Nuances: Navigating the complexities and regulations associated with healthcare data (if product-facing AI involves patient data) requires careful consideration.

• Scaling AI Infrastructure: As AI initiatives grow, ensuring the underlying infrastructure is scalable, cost-effective, and reliable will be an ongoing challenge.

Learning & Development Opportunities:

• Advanced AI/LLM Techniques: Deep dive into state-of-the-art LLM applications, prompt engineering, RAG, and fine-tuning.

• AI System Architecture & Operations: Gain expertise in building robust, observable, and scalable AI systems, bridging ML engineering and MLOps.

• Healthcare Tech Domain: Develop a specialized understanding of the healthcare industry, its workflows, and its unique technological challenges.

• Cross-Functional Leadership: Hone skills in stakeholder management, communication, and influencing across diverse business functions.

• Mentorship & Team Building: Contribute to growing the AI team and developing best practices, potentially leading to future leadership roles.

📝 Enhancement Note: The challenges presented are typical for senior roles in fast-growing tech companies, particularly those focused on AI and operational improvement. The growth opportunities are well-aligned with advancing technical expertise and developing leadership capabilities, offering a clear career trajectory.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex end-to-end AI system you built. What were the key components, challenges, and how did you measure its success and impact?" (Focus on your role, metrics, and operational outcomes).

• "How would you approach designing an AI system to automate X internal workflow for Prompt (e.g., customer support ticket categorization, sales lead qualification)? What metrics would you prioritize?" (Demonstrate analytical thinking and understanding of business needs).

Company & Culture Questions:

• "Why are you interested in Prompt and our mission to improve healthcare through AI?" (Show genuine interest in the company's domain and goals).

• "How do you approach collaborating with non-technical stakeholders to understand their needs and deliver AI solutions they will adopt?" (Assess communication and partnership skills).

Portfolio Presentation Strategy:

• Structure around Impact: Organize your portfolio presentations around specific problems solved, the AI solutions developed, and most importantly, the quantifiable results and business impact achieved.

• Quantify Everything: For each project, clearly state the metrics used, the baseline performance, and the improvements achieved (e.g., "Reduced processing time by 30%", "Increased accuracy from 75% to 92%", "Saved estimated $X in operational costs annually").

• Showcase End-to-End Process: For key projects, briefly outline the ideation, prototyping, development, deployment, and monitoring phases.

• Highlight LLM and Analytics Skills: Ensure your examples explicitly demonstrate your proficiency with LLMs and your ability to design and implement robust evaluation and analytics frameworks.

• Be Ready for Deep Dives: Prepare to discuss technical details, design choices, and trade-offs for any project you present.

📝 Enhancement Note: The interview preparation advice focuses on demonstrating a blend of deep technical AI/ML skills with a strong operational mindset, emphasizing quantifiable results and stakeholder collaboration. Candidates should be ready to discuss not just what they built, but why and how it delivered value.

📌 Application Steps

To apply for this operations-aligned AI position:

• Submit your application through the provided link on Ashby.

• Portfolio Customization: Curate your resume and any supplementary materials (like a personal website or GitHub profile) to prominently feature your experience with end-to-end AI/ML systems, LLMs, rapid prototyping, and crucially, the analytics and metrics used to measure performance and impact.

• Resume Optimization: Ensure your resume clearly highlights achievements using quantifiable data related to efficiency gains, cost reductions, or performance improvements driven by your AI/ML work. Use keywords such as "AI systems," "LLM," "evaluation metrics," "production ownership," "Python," "AWS," and "stakeholder management."

• Interview Preparation: Practice articulating your experience with specific case studies that demonstrate your ability to build, evaluate, and iterate on AI solutions. Be prepared to discuss how you would approach challenges related to operational efficiency and data analysis at Prompt.

• Company Research: Thoroughly research Prompt's mission, products, and the healthcare rehab therapy market. Understand their B2B software focus and how AI can be strategically applied to enhance their offerings and internal operations.

⚠️ 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.