Inovalon is a leading cloud-based healthcare technology company that leverages data analytics and AI to drive meaningful improvements across the healthcare ecosystem. The Senior Full-Stack Machine Learning Engineer sits within the Insights Business Unit, which serves as Inovalon’s central AI and machine learning hub. This team partners with Provider, Payer, and Pharmacy business units to identify, build, and deploy AI solutions that improve clinical and operational outcomes at scale. 

In this role, you will contribute to both classical machine learning and generative AI applications, including LLM-based and agentic solutions. You will work across the full model development lifecycle on a modern, cloud-native AWS stack, collaborating closely with AI Product Managers and a distributed team of senior engineers across the U.S. and India. 

Key Responsibilities

• Design, train, and deploy machine learning models spanning classical ML (classification, regression, clustering, time-series) and generative AI use cases including LLM-based and agentic applications. 
• Build and maintain cloud-native solutions on AWS using containerized architectures (Docker, Kubernetes) to support scalable model serving and data pipelines. 
• Own and contribute to the full Model Development Lifecycle (MDLC), including dataset versioning, model versioning, model registry management, and model evaluation frameworks. 
• Develop and integrate Python-based ML components that work seamlessly with existing product platforms across multiple business units. 
• Collaborate with AI Product Managers across the Insights BU and partner business units (Provider, Payer, Pharmacy) to translate business needs into AI solutions. 
• Apply neural networks and deep learning techniques using PyTorch for appropriate use cases alongside scikit-learn-based classical approaches. 
• Write robust, production-ready code following engineering best practices; participate in code and design reviews. 
• Leverage AI coding tools (such as Claude Code or equivalent) as part of your daily development workflow to improve velocity and code quality. 
• Mentor junior engineers and contribute to team knowledge-sharing around ML best practices, tooling, and architecture decisions. 
• Support integration of frontend components into ML-powered features where applicable. 
• Contribute to retrospectives and team process improvements; actively participate in sprint planning and end-of-iteration demos. 
• Adhere to all HIPAA, data governance, confidentiality, and regulatory requirements in all aspects of work. 
• Maintain compliance with Inovalon’s policies, procedures, and mission statement, fulfilling responsibilities that support operational and financial success. 

Qualifications

Required 

• Minimum 5 years of software development experience with a strong foundation in machine learning fundamentals and model training. 
• Expert-level Python proficiency; Python is the team’s primary language and is the highest-priority technical requirement. 
• Hands-on experience building and deploying classical ML models in production using scikit-learn. 
• Demonstrated experience with generative AI, LLMs, or agentic application development. 
• Proficiency with PyTorch and neural network architectures. 
• Practical knowledge of the Model Development Lifecycle (MDLC): dataset versioning, model versioning, model registry, and model evaluation. 
• AWS cloud experience, including deploying and managing cloud-native workloads. 
• Containerization experience with Docker and/or Kubernetes. 
• Strong problem-solving ability; demonstrated capacity to work independently and take ownership of complex technical challenges. 
• Daily usage of AI-assisted coding tools (e.g., Claude Code, GitHub Copilot, or similar) as part of standard development workflow. 

Preferred 

• Experience with database technologies (SQL or NoSQL); familiarity with data pipeline tooling. 
• Frontend development skills to support full-stack ML feature work. 
• Healthcare domain experience or exposure to HIPAA-regulated environments. 

Education 

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related technical field required. 
• Master’s degree or PhD in Computer Science, Machine Learning, or equivalent practical experience preferred. 

Physical Demands and Work Environment 

• Sedentary work (i.e., sitting for long periods of time). 
• Exerting up to 10 pounds of force occasionally and/or a negligible amount of force. 
• Subject to inside environmental conditions. 
• Travel for this position will include less than 10% locally, usually for training purposes. 

About the Role

We are building a differentiated advisory practice at the intersection of AI strategy, governance, and security. As organizations move from AI experimentation to enterprise-scale deployment, they face urgent questions: How do we govern AI responsibly? How do we secure agentic architectures? How do we align technical capabilities with business strategy and regulatory requirements?

This role is designed for a technically grounded early-career professional who wants to help answer those questions alongside senior practitioners. You will work directly with the practice lead on client-facing engagements, contribute to the development of proprietary frameworks and tools, and build expertise in one of the fastest-growing areas of consulting.

This is not a traditional strategy consulting role, and it is not a pure engineering role. It sits at the intersection—you need to be comfortable reading Python, understanding how LLMs and agentic systems work, AND translating that knowledge into governance frameworks, risk assessments, and executive-ready deliverables. If you thrive at the border between technical depth and business impact, this is for you.

What You Will Do

Client Delivery (50–60%)

• Support the design and delivery of AI governance assessments for clients across industries, leveraging frameworks such as ISO/IEC 42001, NIST AI RMF, and the EU AI Act.
• Conduct technical reviews of client AI systems, architectures, and deployments to identify governance gaps, security vulnerabilities, and risk exposure.
• Develop AI risk registers, control mappings, and maturity assessments tailored to each client’s organizational context and regulatory landscape.
• Prepare client-facing deliverables including assessment reports, executive briefings, implementation roadmaps, and policy recommendations.
• Participate in workshops and stakeholder interviews with client teams ranging from data scientists to C-suite executives.

Practice Development (25–30%)

• Help build and refine the practice’s proprietary AI governance and security framework, integrating industry standards with practical implementation experience.
• Research emerging AI risks, including those specific to agentic AI systems, LLM-based applications, MCP architectures, and AI supply chains.
• Develop reusable tools, templates, and accelerators (e.g., assessment questionnaires, control libraries, risk scoring models) to scale the practice.
• Contribute to thought leadership content: draft LinkedIn posts, white papers, blog articles, and conference presentation materials.
• Monitor regulatory and standards developments (EU AI Act, state-level AI legislation, ISO/IEC updates, OWASP LLM Top 10) and maintain a current knowledge base.

Technical Contribution (15–20%)

• Build proof-of-concept tools and demos using Python to illustrate governance and security concepts for clients (e.g., prompt injection demonstrations, model evaluation dashboards, automated compliance checks).
• Evaluate and test AI platforms, tools, and vendor solutions from a governance and security perspective.
• Support the practice lead’s technical fluency development by preparing technical briefings, annotated code walkthroughs, and “translation” materials that bridge technical and executive audiences.
• Stay hands-on with AI/ML development trends: experiment with agentic frameworks (LangChain, LangGraph, CrewAI), RAG architectures, and model evaluation techniques.

What We Are Looking For

Required Qualifications

• 2–3 years of professional experience in one or more of the following areas: AI/ML engineering, data science, or cybersecurity.
• Bachelor’s degree in Computer Science, Data Science, Information Systems, Cybersecurity, Engineering, or a related field. Master’s degree is a plus but not required.
• Working proficiency in Python and comfort navigating data science tooling (Jupyter, Pandas, Scikit-learn, or equivalent).
• Foundational understanding of machine learning concepts: supervised/unsupervised learning, model training and evaluation, overfitting, bias-variance tradeoff.
• Familiarity with LLM-based applications: understanding of how large language models work (at minimum: tokenization, embeddings, attention, fine-tuning vs. RAG vs. prompt engineering).
• Strong written and verbal communication skills—you must be able to explain technical concepts to non-technical stakeholders clearly and concisely.
• Comfort working in ambiguity: this is a practice being built, not a mature team with fully defined processes. You need to be self-directed and resourceful.

Preferred Qualifications

• Exposure to AI governance or risk management frameworks (ISO/IEC 42001, NIST AI RMF, EU AI Act, OWASP LLM Top 10, MITRE ATLAS).
• Experience with agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen) or understanding of agent architectures and tool-use patterns.
• Understanding of MCP (Model Context Protocol) or similar protocols for AI system integration and the security implications thereof.
• Background in cybersecurity, including familiarity with security frameworks (NIST CSF, ISO 27001) and how they intersect with AI-specific risks.
• Experience in a consulting or professional services environment, including client-facing work and deliverable development.
• Relevant certifications such as: CISA, CISSP, CCSP, AWS/Azure AI certifications, or ISO 42001 Lead Implementer/Auditor.
• Bilingual English/Spanish is a strong plus.

The Kind of Person Who Thrives in This Role

We are not looking for someone who fits neatly into a single box. The ideal candidate is a “bridger”—someone who can move fluidly between a technical deep dive and an executive conversation. Specifically:

• You’re a builder, not just an analyst. When you see a gap in a process or a tool, your instinct is to prototype something—a script, a template, a dashboard, not just write a slide about it.
• You’re curious about “why” and “so what.” You don’t just want to understand how a transformer model works; you want to understand what that means for how organizations should govern and secure it.
• You write well. A significant portion of this role involves producing written work—reports, frameworks, articles—and clarity of writing is non-negotiable.
• You’re comfortable being the least experienced person in the room. You’ll be in meetings with CIOs, CISOs, and senior partners. You need the confidence to contribute and the humility to learn.

What We Offer

• Accelerated growth trajectory: You’ll be building a practice from the ground up alongside senior leadership, gaining exposure and responsibility that would take years to earn in a larger, more established team.
• Investment in your learning: Budget for certifications, training programs, and conference attendance.
• Client diversity: Work across industries and with organizations at different stages of AI maturity—from Fortune 500 companies to mid-market firms navigating their first AI initiatives.