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:

About the role

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:

• multi-step task decomposition and orchestration

• tool and API integration

• structured outputs and validation mechanisms

• Strong mastery of LLM-based system design, including prompt/version management, grounding strategies, and error handling.

• Proven ability to design and implement evaluation frameworks for agentic systems (task success, grounding, hallucination detection, regression testing).

• Strong exposure to MLOps / DevOps practices, including CI/CD, monitoring, observability, cost management, and safe deployment strategies.

• Familiarity with clinical trials or life sciences environments, including structured processes and data quality expectations, preferred.

• Awareness of data privacy and security principles applied to sensitive or regulated data (e.g., healthcare, personal data), preferred.

• Previous contribution to internal platforms or shared engineering frameworks is a plus.

• Strong ability to lead technical initiatives and drive alignment across engineering, product, operations, and quality stakeholders.

• Clear and effective communicator, capable of explaining complex agentic AI systems, risks, and trade-offs to both technical and non-technical audiences.

• Mentorship mindset: actively supports and coaches other engineers through design reviews, pairing, and feedback.

• High level of ownership and accountability; comfortable taking responsibility for design decisions and production outcomes.

• Pragmatic problem-solver with sound engineering judgment, particularly in ambiguous or high-risk scenarios.

• Bias toward delivery while maintaining a strong focus on quality, reliability, and safety.

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.

• 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

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.