AI / ML Prototyping Developer

📍 Job Overview

Job Title: AI / ML Prototyping Developer

Company: Mythics, LLC

Location: United States

Job Type: FULL_TIME

Category: AI/ML Development & Operations

Date Posted: April 22, 2026

Experience Level: 10+ Years

Remote Status: Hybrid

🚀 Role Summary

• Design, develop, and test rapid AI/ML prototypes for a federal program, focusing on mission-critical use cases.

• Integrate custom AI agents and machine learning models into secure, scalable data processing pipelines.

• Analyze and process multi-modal datasets (text, images, structured data) to extract actionable insights and patterns.

• Collaborate closely with technical and mission stakeholders to refine AI use case requirements and ensure compliance with regulated environments.

• Apply secure development practices, including code scanning and dependency analysis, to meet federal compliance standards.

📝 Enhancement Note: This role bridges the gap between advanced AI/ML research and practical application within a federal context. It requires not only strong technical development skills but also an understanding of secure development lifecycles and compliance requirements common in government contracting. The emphasis on "prototyping" suggests a fast-paced, iterative development environment.

📈 Primary Responsibilities

• Spearhead the design, development, and rigorous testing of AI and machine learning prototypes tailored to specific mission-focused use cases.

• Develop, train, evaluate, and meticulously optimize machine learning models using both structured and unstructured datasets to achieve desired performance metrics.

• Seamlessly integrate developed AI/ML models into robust, secure, and scalable data processing pipelines, ensuring efficient data flow and model deployment.

• Support the comprehensive ingestion, processing, and in-depth analysis of multi-modal datasets (e.g., text, images, structured data) to identify critical patterns, emerging trends, and key strategic values.

• Develop and customize sophisticated AI agents, ensuring they are precisely tailored to defined use cases and adhere strictly to all relevant regulatory constraints.

• Foster strong collaborative relationships with data engineers, architects, and diverse stakeholders to continuously refine requirements and strategically evolve AI use cases.

• Diligently apply secure development practices throughout the development lifecycle, including conducting code scanning and dependency analysis to proactively support compliance requirements.

• Create comprehensive documentation for model design, underlying assumptions, identified limitations, and detailed performance results to facilitate technical reviews and audits.

📝 Enhancement Note: The responsibilities highlight a full lifecycle involvement for AI/ML prototypes, from initial concept and model development through integration, analysis, and documentation for compliance. The mention of "mission-focused use cases" and "regulated environments" implies a need for practical, secure, and auditable solutions.

🎓 Skills & Qualifications

Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a closely related technical field (or equivalent professional experience).

Experience: Minimum of 10 years of relevant professional work experience, with a strong emphasis on developing and training machine learning or AI models.

Required Skills:

• Proven hands-on experience prototyping AI-driven solutions, primarily using Python.

• Demonstrated experience in developing, training, and evaluating machine learning or AI models.

• Proficiency with version control systems, specifically Git, for collaborative development workflows.

• Ability to effectively query and work with SQL, including relational and analytical data stores.

• Practical experience integrating AI/ML models into automated or semi-automated data processing pipelines.

Preferred Skills:

• Prior experience developing or customizing AI agents and architecting agent-based workflows.

• Familiarity with AutoML tools and techniques for automated model experimentation and selection.

• Exposure to critical concepts in model governance, including explainability (XAI) and risk mitigation strategies for AI systems.

• Experience working within regulated industries such as the Department of Defense (DoD), Financial Services, healthcare, or other compliance-driven environments.

📝 Enhancement Note: The 10-year experience requirement, coupled with a Bachelor's degree, indicates a senior-level role. The emphasis on Python, Git, and SQL points to a standard development stack, while the preferred qualifications highlight a focus on advanced AI/ML practices and experience within secure, regulated environments, crucial for federal contracts.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing successful AI/ML prototyping projects, highlighting the problem statement, technical approach, and outcomes.

• Include case studies detailing the development and integration of machine learning models into data pipelines, emphasizing efficiency gains or insights derived.

• Provide examples of custom AI agent development, illustrating problem-solving capabilities and adaptability to specific use cases.

• Showcase experience with multi-modal data analysis, presenting methodologies for processing and extracting value from diverse data types.

Process Documentation:

• Present examples of workflow design and optimization for AI/ML model development and deployment pipelines.

• Detail methodologies used for model training, evaluation, and iteration, including experimental design and parameter tuning.

• Illustrate processes for integrating AI/ML models into broader application architectures or data processing systems.

• Document approaches to ensuring model performance, reliability, and adherence to compliance standards within a project lifecycle.

📝 Enhancement Note: Given the role's focus on prototyping and federal compliance, a portfolio should emphasize practical application, iterative development, and adherence to security standards. Demonstrating the ability to translate complex AI/ML concepts into tangible, working prototypes and secure pipelines will be critical.

💵 Compensation & Benefits

Salary Range: Based on industry benchmarks for AI/ML Prototyping Developers with 10+ years of experience, in the United States, requiring a Secret clearance, the estimated annual salary range is $130,000 - $180,000. This range can vary based on specific location, candidate qualifications, and the exact nature of the federal program.

Benefits:

• Comprehensive Health, Dental, and Vision insurance plans.

• Premier 401k retirement plan with corporate matching.

• 529 college savings plan.

• Tax-advantaged Health Savings Account (HSA).

• Dependent Care Flexible Spending Account (FSA).

• Generous Paid Time Off (PTO) bank, plus paid holidays and additional flex time off.

• Employee referral program with potential rewards.

• Employee recognition, gift, and reward programs.

• Tuition reimbursement for continuing education.

• Legal Resources.

Working Hours: Standard full-time work requires approximately 40 hours per week. While the role is hybrid, specific on-site requirements may vary. The nature of prototyping and federal program support may necessitate occasional flexibility outside standard hours to meet project deadlines or critical milestones.

📝 Enhancement Note: The salary estimate is based on typical compensation for senior AI/ML engineers in the US, adjusted upwards due to the stringent clearance requirement and specialized nature of federal contracting. The benefits package is extensive, reflecting a company culture that prioritizes employee well-being and professional development, which is attractive for long-term career commitments.

🎯 Team & Company Context

🏢 Company Culture

Industry: Mythics, LLC operates as an award-winning Oracle systems integrator, consulting firm, managed services provider, and elite Oracle platinum resale partner. Their business model is centered on deep expertise in Oracle technologies and business processes, offering procurement and integration services across Oracle's cloud, software, support, hardware, and engineered systems.

Company Size: Mythics is a medium-sized enterprise, likely employing between 201-500 individuals. This size often fosters a balance between established processes and a more agile, collaborative environment where individual contributions are highly visible.

Founded: Founded in 2000, Mythics has over two decades of experience in the IT solutions and services sector, building a strong reputation and client base, particularly within the federal government space. This longevity suggests stability and a deep understanding of client needs.

Team Structure: The AI/ML Prototyping Developer will likely be part of a specialized technical team, possibly within a larger solutions or engineering division. This team would typically include data engineers, solution architects, and other developers, reporting to a technical lead or program manager. Collaboration with client-side technical and mission stakeholders is a key aspect of the role.

Methodology: The company's focus on Oracle systems integration suggests a structured, process-oriented approach to project delivery. For this AI/ML role, it implies a blend of agile prototyping methodologies for rapid iteration and more formal, compliance-driven processes for development, testing, and deployment within federal environments.

Company Website: www.mythics.com

📝 Enhancement Note: Mythics' specialization in Oracle and federal contracting positions the AI/ML Prototyping Developer role within a domain requiring a high degree of technical precision, security consciousness, and client-focused problem-solving. The company's established presence suggests opportunities to work on significant, long-term government projects.

📈 Career & Growth Analysis

Operations Career Level: This role represents a senior individual contributor position within the AI/ML and software development domain. It requires deep technical expertise in AI/ML prototyping and development, coupled with the ability to operate effectively within a federal contracting environment. The focus is on technical execution, innovation in prototyping, and adherence to strict operational and security standards.

Reporting Structure: The AI/ML Prototyping Developer will likely report to a Technical Lead, Project Manager, or a Director of Engineering/Solutions. They will collaborate closely with cross-functional teams including data engineers, system architects, cybersecurity specialists, and client stakeholders. The structure emphasizes direct contribution to project deliverables and client success.

Operations Impact: The role's direct impact is on the successful development and integration of cutting-edge AI/ML capabilities for federal programs. This can significantly enhance mission effectiveness, improve operational efficiencies, and provide critical data insights for decision-making within government agencies. The prototyping nature means the impact is also in de-risking future, larger-scale AI/ML implementations.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI/ML domains (e.g., NLP, computer vision, agent-based systems) and become a subject matter expert within Mythics.

• Leadership Development: Transition into technical lead or management roles, guiding teams of developers and overseeing project execution for AI/ML initiatives.

• Cross-Functional Expertise: Gain broader knowledge of federal program requirements, system integration, and cybersecurity, expanding capabilities beyond core AI/ML development.

• Advanced Certifications: Pursue industry-recognized certifications in AI/ML, cloud technologies, or cybersecurity relevant to federal standards.

📝 Enhancement Note: The career path for this role is strongly tied to technical mastery and successful project delivery within the federal sector. Growth is likely to involve increasing responsibility for complex projects, team leadership, and strategic technical contributions, rather than a shift away from core technical competencies.

🌐 Work Environment

Office Type: The role is described as Hybrid, indicating a blend of remote work and in-office presence. Mythics emphasizes an innovative and collaborative workplace, suggesting that in-office days will be geared towards team collaboration, brainstorming, and client interactions.

Office Location(s): While specific office locations are not detailed, Mythics has a strong presence in the U.S. federal contracting ecosystem, suggesting potential office hubs in or near major government centers or technology corridors. Candidates should inquire about specific office locations and hybrid expectations.

Workspace Context:

• The workspace is designed to foster innovation and collaboration, supporting the rapid prototyping nature of the role.

• Access to necessary development tools, secure computing environments, and potentially specialized hardware for AI/ML model training will be provided.

• Opportunities for direct interaction with technical colleagues, project managers, and potentially clients will be available, especially during in-office days.

Work Schedule: The standard work schedule is approximately 40 hours per week. However, the demands of federal projects and the nature of prototyping may require flexibility, with potential for occasional extended hours to meet critical deadlines or support urgent client needs.

📝 Enhancement Note: The hybrid work model offers flexibility, but candidates should be prepared for the collaborative and compliance-focused demands of working within a federal contractor environment, which often involves a structured approach to on-site presence and project engagement.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will likely conduct an initial phone screen to assess basic qualifications, clearance eligibility, and cultural fit.

• Technical Interview(s): Expect one or more technical interviews focusing on AI/ML concepts, Python development, SQL, Git, and experience with prototyping. This may involve coding challenges or architecture discussions.

• Portfolio Review: Candidates will be asked to present their portfolio, detailing specific projects, their role, technical challenges, and outcomes. Be prepared to discuss your contributions in detail.

• Behavioral/Situational Interview: Assess problem-solving skills, collaboration style, ability to work in a regulated environment, and communication with technical and non-technical stakeholders.

• Security Clearance Verification: The process will involve vetting for U.S. Citizenship and initiating the security clearance process.

Portfolio Review Tips:

• Focus on Impact: For each project, clearly articulate the problem, your specific contributions, the AI/ML techniques used, and the measurable outcomes or insights generated.

• Technical Depth: Be ready to discuss the nuances of your model development, data processing pipelines, and any challenges encountered and overcome.

• Security Awareness: Highlight any aspects of your projects that demonstrate an understanding of secure coding, data handling, and compliance requirements, especially if related to regulated data.

• Clarity and Conciseness: Structure your presentation logically and be prepared to answer detailed technical questions.

Challenge Preparation:

• Coding Exercises: Practice Python coding challenges, particularly those involving data manipulation (Pandas), algorithm implementation, and basic ML model integration.

• System Design: Be prepared for questions about designing data pipelines for AI/ML models, considering scalability, security, and efficiency.

• Scenario-Based Questions: Think about how you would approach common AI/ML development challenges in a federal context, such as working with sensitive data or integrating with legacy systems.

📝 Enhancement Note: The interview process will heavily weigh technical proficiency in AI/ML development, practical experience, and the ability to navigate the stringent requirements of federal contracting, including security clearance and compliance. A well-prepared portfolio demonstrating hands-on experience is paramount.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Language: Python is the primary language for AI/ML development and data processing.

• Version Control: Git for source code management and collaborative development.

• Data Querying: SQL for interacting with relational and analytical data stores.

• AI/ML Libraries: Hugging Face libraries (for NLP and multi-modal use cases), along with common ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

• Prototyping/Development Environments: Jupyter Notebooks, IDEs like VS Code or PyCharm.

Analytics & Reporting:

• Data Analysis Libraries: Pandas, NumPy for data manipulation and analysis.

• Model Evaluation Tools: Scikit-learn's metrics, custom evaluation scripts.

• Dashboarding (Potential): While not explicitly stated, familiarity with tools like Tableau, Power BI, or custom dashboarding in Python (e.g., Dash, Streamlit) could be beneficial for presenting prototype results.

CRM & Automation:

• Data Pipelines: Experience with tools for building and managing data pipelines (e.g., Apache Airflow, cloud-native services) is implied by the "integrate AI/ML models into secure and scalable data processing pipelines" requirement.

• Security Scanning Tools: Familiarity with OWASP ZAP, Bandit, Semgrep, and general dependency scanning tools is explicitly required for compliance.

📝 Enhancement Note: The technology stack is focused on modern AI/ML development practices, with a strong emphasis on Python and its associated libraries. The inclusion of security scanning tools underscores the critical importance of secure development within the federal program context.

👥 Team Culture & Values

Operations Values:

• Respect, Empathy, Excellence, and Fun (REEF): This core value system drives Mythics' commitment to a positive, collaborative, and high-achieving workplace.

• Excellence: A drive for delivering exceptional customer service and high-quality technical solutions is paramount, especially within the federal contracting space.

• Collaboration: Emphasis on teamwork and cross-functional cooperation to achieve shared project goals and client success.

• Innovation: A culture that encourages rapid prototyping and the exploration of new AI/ML capabilities to solve complex problems.

• Compliance & Security: A foundational value, particularly for federal programs, requiring strict adherence to data handling, coding, and security standards.

Collaboration Style:

• Cross-functional Integration: The role requires close collaboration with data engineers, architects, and potentially cybersecurity teams to ensure seamless integration of AI/ML solutions.

• Stakeholder Communication: Effective communication with both technical peers and non-technical mission stakeholders is essential for refining requirements and demonstrating prototype value.

• Iterative Development: A willingness to engage in rapid prototyping, receive feedback, and iterate on solutions is key to success in this role.

📝 Enhancement Note: The "REEF" values suggest a company culture that balances professional rigor with a positive work environment. For this role, the "Excellence" and "Compliance & Security" values are particularly critical due to the federal program context.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapid Prototyping in a Regulated Environment: Balancing the speed required for prototyping with the stringent security and compliance demands of a federal program.

• Multi-Modal Data Integration: Effectively processing and extracting insights from diverse data types (text, images, structured data) can be technically complex.

• AI Agent Customization: Developing highly tailored AI agents that meet specific use cases and regulatory constraints requires deep understanding and careful implementation.

• Stakeholder Alignment: Ensuring clear communication and alignment on AI use cases and prototype capabilities with both technical and non-technical federal stakeholders.

Learning & Development Opportunities:

• Cutting-Edge AI/ML: Opportunities to work with advanced AI/ML models, libraries (like Hugging Face), and techniques in a practical, mission-driven setting.

• Federal Domain Expertise: Gain invaluable experience and insight into the specific needs, challenges, and operational environments of federal government programs.

• Secure Development Practices: Enhance skills in secure coding, vulnerability analysis, and compliance reporting, which are highly transferable.

• Career Advancement: Potential to grow into lead engineering roles, program management, or specialized AI/ML architecture positions within Mythics' federal contracting division.

📝 Enhancement Note: The primary challenges revolve around the intersection of advanced AI/ML development and the unique demands of federal projects. Growth opportunities are strong for individuals who can master these complexities and contribute to impactful government solutions.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex AI/ML prototyping project you led. What were the key challenges, your approach to overcoming them, and the final outcome?" (Focus on process, problem-solving, and results).

• "How do you ensure the security and compliance of AI/ML models and data pipelines, especially when working with sensitive federal data?" (Highlight knowledge of secure coding, scanning tools, and regulatory awareness).

Company & Culture Questions:

• "Why are you interested in working for Mythics, and specifically on federal programs?" (Connect your motivations to Mythics' mission and the role's impact).

• "How do you embody Mythics' core values of Respect, Empathy, Excellence, and Fun (REEF) in your work?" (Provide specific examples of how you demonstrate these values in a professional setting).

Portfolio Presentation Strategy:

• Structure Your Narrative: For each project, use a STAR method (Situation, Task, Action, Result) or similar framework. Clearly define the problem, your role and actions, and the impact of your work.

• Show, Don't Just Tell: Use diagrams, code snippets (if appropriate and anonymized), and mock-ups to illustrate your technical approach and the functionality of your prototypes.

• Quantify Results: Wherever possible, use metrics to demonstrate the success of your prototypes (e.g., accuracy improvements, efficiency gains, reduction in processing time).

• Highlight Security & Compliance: Explicitly point out how security considerations were integrated into your development process and documentation.

📝 Enhancement Note: Interview preparation should focus on showcasing technical depth, practical experience with AI/ML development and integration, and a clear understanding of the security and compliance requirements inherent in federal contracting. The portfolio presentation is a critical component.

📌 Application Steps

To apply for this AI / ML Prototyping Developer position:

• Submit your application through the provided link on the Mythics careers portal.

• Portfolio Customization: Tailor your resume and portfolio to highlight AI/ML prototyping experience, Python development, Git usage, SQL proficiency, and any relevant federal or regulated industry work. Emphasize projects involving multi-modal data, AI agents, and secure data pipelines.

• Resume Optimization: Ensure your resume clearly states your 10+ years of experience, technical skills, and U.S. citizenship status. Use keywords from the job description like "AI," "Machine Learning," "Python," "prototyping," "data pipelines," and "Secret Clearance."

• Interview Preparation: Practice articulating your experience with AI/ML model development, integration, and secure coding. Prepare specific examples from your past projects that align with the job responsibilities and potential interview questions.

• Company Research: Familiarize yourself with Mythics, LLC, their work with Oracle, and their role as a federal contractor. Understand their REEF values and how your approach to work aligns with their culture. Research common challenges in federal AI/ML deployments.

⚠️ 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.