AI / ML Engineer

Job Responsibilities

• Working on component design, development, integration, and standardization to create AI-driven solutions that seamlessly integrate into clinical practice to enhance patient care and clinic operations.
• Collaborating with a multidisciplinary team, including clinicians, user experience designers, product managers, and IT professionals, to understand user needs, workflows, and clinical requirements and assess feasibility.
• Translating user feedback and requirements into design concepts and usability specifications for AI solutions.
• Interpreting/analyzing data to inform strategic decisions and communicate complex findings in easily understandable terms to bridge the gap between AI technologies and clinical applications.
• Leveraging machine learning techniques such as deep learning, natural language processing, computer vision, large language models, etc., to design, develop, and deploy end-to-end AI solutions for healthcare applications.
• Participating in the engineering of systems crucial for developing and deploying AI solutions.
• Facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.
• Contributing to implementing the best practices and standards for AI development and deployment methodologies, tools, and platforms.
• Providing training and education to healthcare staff on the use of AI tools and technologies.

Required Skills & Experience

• AI/ML software engineers to design and build production AI systems for healthcare.
• The role spans AI system design (agent architectures, evaluation, guardrails) and production software engineering (Python services, data pipelines, cloud deployment).
• We are hiring multiple contractors; specific strengths can differ across candidates.
• Core Responsibilities: Design and implement Agentic AI systems — LLM integrations, prompt engineering, MCP servers, agent architectures.
• Build and maintain Python services, automation workflows, and data pipelines (including RAG with embeddings and vector databases).
• Implement evaluation frameworks and guardrails for LLM/agent systems before production.
• Deploy, monitor, and optimize ML/AI solutions in the cloud.
• Collaborate with product, data, and engineering teams; uphold code quality, performance, security, and maintainability.
• Technical Requirements: Experience: 7 years of software/ML engineering, with recent hands-on AI/LLM work.
• Python: Advanced; production experience with APIs, async, and testing.
• AI / LLM agents: Designing and implementing autonomous or semi-autonomous agents (tool-using, planners, orchestrators).
• Agent frameworks: Hands-on with at least one (LangChain, LangGraph, LlamaIndex, Semantic Kernel, Google ADK).
• MCP: Agent communication, coordination, or protocol-driven AI architectures.
• Evaluation & guardrails: Prompt regression tests, hallucination and quality metrics, and guardrails for PII, jailbreaks, and unsafe outputs.
• ML lifecycle: ML pipelines, deployment, evaluation, monitoring; embedding models, vector DBs, and RAG.
• Data management: Modeling, pipelines, SQL/NoSQL, data quality and governance at scale.
• Cloud: Hands-on in Azure, AWS, or GCP; cloud-native deployment patterns and CI/CD.
• HIPAA / PHI: Working knowledge of PHI handling in AI — BAA-covered model endpoints, no PHI in training data or logs, de-identification before prompt context.
• Preferred Technical Skills: AI/LLM Agent and MCP tooling – Google ADK, Copilot Studio.
• Cloud Experience – Google Cloud or Azure preferred.
• Database Knowledge – BigQuery, Firestore, Cloud SQL, etc.
• Data pipeline – Dataflow.
• Power Automate.
• Automation Tooling – UiPath, etc.
• CI/CD Pipeline – Azure DevOps Pipeline.
• Infrastructure as Code (IaC) – Terraform.