Senior Agentic AI Engineer

Lead discovery and design discussions with Product, Imaging Operations, Clinical Ops, QA, Security, and Regulatory teams.

Translate complex clinical and PDLC workflows into robust, agent‑compatible system designs with clearly defined boundaries, validation rules, and human oversight.

Drive alignment between business objectives, product strategy, and technical implementation.

Act as a technical point of contact for stakeholders on agentic AI capabilities and limitations.

Lead technical design reviews for agentic AI features and platform components.

Mentor and coach mid‑level engineers on agent design, evaluation strategies, failure analysis, and production readiness.

Define team‑level best practices for prompt engineering, retrieval grounding, evaluation metrics, and observability.

Contribute to internal documentation, reference implementations, and shared libraries.

Design and build end‑to‑end agentic workflows involving planning, tool use, retrieval, and structured outputs.

Implement advanced RAG patterns grounded in controlled knowledge sources (protocols, SOPs, specifications, audit artifacts).

Develop evaluation frameworks to measure task success, grounding quality, hallucination risk, and regression across agent versions.

Build scalable backend services, APIs, and orchestration layers supporting agent execution in production.

Own production deployments of agentic systems, including CI/CD pipelines, environment promotion, and rollback strategies.

Define observability standards: tracing agent reasoning, tool calls, retrieval events, errors, and cost telemetry.

Ensure systems meet security, privacy, and compliance requirements (access controls, audit logs, data protection).

Partner with platform and DevOps teams to improve reliability and scalability of AI services.

Carry out any other reasonable duties as requested

Solid professional software engineering experience.

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

Deep practical experience with retrieval-augmented generation (RAG) patterns, including chunking strategies, embedding management, retrieval quality evaluation, and traceability.

Experience building scalable backend services, APIs, and pipelines supporting AI-driven workflows.

Experience working with or strong understanding of medical imaging workflows (e.g., DICOM metadata, modality concepts, imaging QC processes), preferred.

Experience developing software in regulated or quality-sensitive environments, with attention to validation, auditability, and traceability, preferred.

Demonstrated ownership of complex systems from design through production.

Proven ability to lead technical initiatives and mentor engineers.

Strong experience operationalizing AI systems at scale.

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.

English: Fluent

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