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Job Description
Role: AI/ML Engineer Location: Remote Duration: 1 year Rate: $78.20/hour W2
Role Summary: 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.
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.
Required Education: - Master's degree in Engineering, Computer Science, mathematics, health science, or a related field AND one (1) year experience. (Escalate for approval if Master's degree is not in any of the specified fields of study).
- OR
- Bachelor's degree with three (3) years of experience.
- OR
- HS Diploma/GED with Seven (7) years of experience may be considered.
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