Agentic AI Engineer

Job description

We’re on a mission to change the future of clinical research. At Perceptive, we help the biopharmaceutical industry bring medical treatments to the market, faster. Our mission is to change the world but to do this, we need people like you. What can we offer you? Apart from job satisfaction, we can offer you: YOURSELF • 25 days’ holiday (with the option to buy more) HEALTH • Health Cash Plan • Optional private health, dental insurance, and health screens • Cycle to work scheme WEALTH • Generous pension scheme with up to 10% employer contribution • Life assurance • Season ticket loan About the role 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 C ross 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. 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. Participate in design reviews and provide guidance on selecting frameworks and architectures. Coach peers through code reviews, pairing, demos, and knowledge-sharing sessions. 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. Prototype and validate new capabilities quickly, then harden into production-ready features. Run experiments comparing models, prompts, retrieval strategies, and agent architectures; document results and recommendations. Identify opportunities to standardize and reuse agent components across clinical imaging workflows and PDLC automation. 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 Certifications 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. Come as you are. We're proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, colour, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.