Senior Agentic AI Engineer
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As Senior Agentic AI Engineer, you will own the design, implementation, and operationalization of complex agentic AI systems that drive automation, scalability, and data quality across medical imaging clinical trials. This role leads the delivery of multi‑step, production‑grade agentic workflows supporting imaging intake, quality control, protocol compliance, data validation, and structured decision support. The Senior Agentic AI Engineer also plays a key role in advancing the company’s AI‑driven Product Development Life Cycle (PDLC)—from discovery and design automation to delivery, deployment, and production operations.
In addition to hands‑on technical contributions, this role provides technical leadership, mentoring engineers, shaping best practices for agent reliability and evaluation, and ensuring AI systems meet quality, safety, and regulatory expectations.
Key Responsibilities
Cross functional collaborations
- 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.
Technical leadership & Mentorship
- 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.
Algorithm and Software Development
- 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.
Deployment and integration (MLOps)
- 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.
Innovation & Research application
- Evaluate emerging agentic AI techniques and assess their applicability to regulated clinical workflows.
- Lead proof‑of‑concepts and transition validated approaches into hardened, production‑ready solutions.
- Establish guidelines for selecting models, architectures, and evaluation methods.
- Contribute to longer‑term AI roadmap planning and technical strategy.
Other
- Carry out any other reasonable duties as requested
Functional Competencies (Technical knowledge/Skills)
- Advanced software engineering skills with a strong track record of delivering production-grade systems in complex environments.
- Hands-on expertise designing and implementing agentic AI systems, including:
Experience, Education, and Certifications
- 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.