AI/ML ENGINEER

Job Summary:

AI/ML Engineers at *** play a pivotal role in the union of data, systems, and computer sciences. They work closely with a multidisciplinary team, including clinicians, user experience designers, product managers, IT professionals, and external partners, to develop and deploy effective, efficient, and ethical AI/ML solutions into clinical practice to enhance patient care and operational efficiency. As an AI/ML Engineer, you may work on the full spectrum of the AI life cycle from ideation to production. You understand the clinical environment well, including workflows, challenges, and requirements of healthcare providers and patients. You will leverage advanced techniques in AI/ML to analyze vast amounts of healthcare data, including patient records, medical imaging, and genomic information, to develop AI solutions that meet clinical needs and are integrated smoothly into clinical processes. You will develop, integrate, and standardize software components and create, maintain, and follow quality system procedures. You will work on the engineering of systems that are pivotal to developing and deploying these solutions, which encompass everything from design requirements, development, component creation, verification, non-clinical validation, and risk mitigation to ensure our digital health technology products meet and exceed regulatory requirements and setting new benchmarks for safety and effectiveness in clinical settings. Your expertise will also extend to facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.

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 Google Cloud Platform; 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.

Other Requirements:

• Rapid experimentation: AI moves fast; continuously evaluates new models, capabilities, and emerging patterns (MCP, A2A, agent frameworks).
• Healthcare context: AI/ML in this environment requires healthcare grounding, not generic model building.
• Proactive: Proposes AI-assisted solutions; tests what is possible and shares findings.
• Independent operator: Works with minimal supervision in fast-moving environments; strong documentation and cross-functional collaboration.
• MLOps or LLMOps experience.
• Streaming or event-driven architectures.
• Prior enterprise or large-scale data management.