Senior ML Engineer: GenAI, RAG & NLP for HealthTech

Capital Rx

Charlotte (NC)

Hybrid

USD 135,000 - 169,000

Full time

14 days+

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Job summary

Judi Health in New York, NY is seeking a Senior Machine Learning Engineer to design and deploy AI-driven systems for pharmacy benefits, leveraging NLP, GenAI, and RAG-based QnA. You will build scalable Python backends, APIs, and multi-agent orchestration patterns in a fast-paced startup setting.

Candidates should have 4+ years in ML, strong Python and PyTorch skills, and experience with cloud platforms and containerized deployments.

Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related quantitative field.
Minimum 4+ years of experience in industry with a strong focus on ML solutions development and production deployment.
Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development.
Strong proficiency in Python and familiarity with ML libraries such as PyTorch.
Statistical Machine Learning and AI in NLP, Speech, RAG, Virtual Agents, and/or Document Processing tech
Familiarity with cloud platforms such as AWS or Azure, including hands-on experience with containerized deployments
Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma; experience prompting GenAI services
Experience designing evaluation frameworks for non-deterministic/agentic AI systems
Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking
Ability to work across multiple disciplines in a fast-paced startup environment
Experience with microservices/API-based application paradigm
Good written communication skills for remote collaboration

Responsibilities

Design, develop, and productionize ML solutions in fields like Document understanding, Search and QnA, GenAI, Virtual Agents.
Develop and maintain backend services using Python, focusing on AI-driven applications.
Design and implement APIs for integration with AI models and services.
Design multi-agent orchestration patterns and evaluate architectural tradeoffs for scaling agent development.
Contribute to retrieval/search infrastructure underlying RAG systems; build and maintain CI/CD pipelines for AI systems.
Collaborate with product managers, engineers, and stakeholders as ML SME.
Work across the full ML lifecycle from problem definition to deployment.
Participate in code reviews, testing, and QA processes.
Troubleshoot and resolve issues related to AI models, integration, deployment and backend services.
Implement telemetry, A/B testing and validation for model effectiveness; ensure robustness of ML solutions.
Document and communicate development processes, system designs, and architectural decisions.
Ensure HIPAA compliance for healthcare data.