Agentic AI Engineer

As Agentic AI Engineer, you will design, build, and operationalize agentic AI capabilities to increase automation, improve data quality, and accelerate decision-making within medical imaging clinical trials. This role focuses on building AI agents that can reliably execute workflows such as intake and validation of imaging data, metadata reconciliation, protocol-driven checks, quality control support, and structured extraction from multi-modal artifacts (DICOM, non‑DICOM, reports, PDFs). In parallel, the role will help transform the company’s Product Development Life Cycle (PDLC) into an AI-driven lifecycle—enabling agentic workflows from Product Discovery (e.g., requirements synthesis, research, backlog shaping) to Delivery and Deployment (e.g., test generation, release readiness, documentation automation) through Production Operations (e.g., incident triage, observability insights, automated runbooks), while ensuring safety, compliance, traceability, and human oversight.

Key Responsibilities

Cross functional collaborations

• Partner with Product, Imaging Ops, Data Engineering, QA, Security, and Regulatory/Quality to identify high-value automation opportunities and define agentic AI use cases
• Translate clinical-trial imaging workflows and PDLC processes into agent-ready task models (inputs, outputs, constraints, guardrails, acceptance criteria)
• Work with SMEs to define quality rubrics, evaluation datasets, and human review workflows to validate agent outputs
• Support change management: help teams adopt agentic features with clear UX patterns, onboarding, and documentation

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Technical ownership

• 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

Algorithm and Software Development

• 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

Deployment and integration (MLOps)

• 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

Innovation & Research application

• Stay current on agentic AI patterns (planning, tool use, memory, multi-agent coordination, structured outputs) and evaluate feasibility for regulated workflows

Other

• Carry out any other reasonable duties as requested

Functional Competencies (Technical knowledge/skills)

• Strong software engineering skills (clean architecture, APIs, testing, code reviews)
• Solid grounding in RAG and knowledge grounding patterns (chunking, embeddings, retrieval quality, citations/traceability)
• Familiarity with MLOps / DevOps: CI/CD, monitoring, logging, secure deployment practices
• Data handling fundamentals: schema design, data validation, data lineage, and auditability
• Exposure to medical imaging concepts (DICOM, metadata, modality basics) and/or clinical trial workflows, preferred
• Working knowledge of regulated software expectations (validation mindset, audit trails, traceability), preferred
• Familiarity with privacy/security principles for sensitive data, preferred
• Strong communication skills; can explain technical trade-offs to non-technical stakeholders
• Comfortable working across teams and iterating with end users
• Bias for action: can prototype quickly and then productionize reliably
• High ownership and accountability; pragmatic approach to risk and quality

Experience, Education, and Certifcations

• icationsSolid 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).