Agentic AI Engineer

Promote engineering best practices for agentic systems: reliability, observability, evaluation, reproducibility, and secure-by-design patterns.

Contribute to internal standards for: prompt/version control, agent tools/plugins, retrieval configuration, grounding/citations, and human-in-the-loop review.

Participate in design reviews and provide guidance on selecting frameworks and architectures.

Coach peers through code reviews, pairing, demos, and knowledge-sharing sessions.

Build agentic workflows (multi-step reasoning + tool use) that support imaging trial processes such as:

data intake and validation (DICOM/non‑DICOM), metadata consistency checks

protocol-driven QC support and discrepancy detection

data completeness checks and structured issue creation

automated summarization of imaging packets and context generation for reviewers

Implement retrieval-augmented generation (RAG) patterns grounded in controlled knowledge sources (SOPs, protocols, specs, runbooks), with traceability.

Develop evaluation harnesses for agent behavior: task success rate, hallucination/grounding checks, regression testing, and bias/failure-mode analysis.

Contribute production-quality code (APIs, services, pipelines) with strong testing, documentation, and maintainability.

Package and deploy agentic services using approved cloud and SDLC patterns: CI/CD, infrastructure-as-code, secret management, and environment promotion.

Implement guardrails and controls: access control, tool permissions, sandboxing, content filtering, rate limits, and audit logging.

Build observability: traces, structured logs, evaluation telemetry, cost monitoring, and error analytics (agent runs, tool calls, retrieval hits, model performance).

Ensure safe production operations: rollback strategies, feature flags, canary releases, and incident playbooks.

Carry out any other reasonable duties as requested.

Solid professional software engineering experience, including shipping production systems.

Experience implementing LLM-based systems (agentic workflows, tool calling, structured outputs).

Practical experience with evaluation of LLM/agent quality (offline tests + production telemetry).

Demonstrated experience building or integrating AI-powered applications (LLMs, NLP, decision support, automation).

Experience working in cross-functional environments with product and operational teams.

Demonstrated experience designing systems with reliability and safety in mind (testability, monitoring, clear failure modes).

Experience in healthcare/life sciences, clinical trials, imaging workflows, or regulated environments is a plus.

Master’s degree in Computer Science, Engineering, Data Science, or related field or equivalent practical experience.

Master’s degree or relevant AI/ML specialization is a plus.

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