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AstraZeneca
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Senior Applied AI & ML Engineer - Evinova
ICT
Company shared salary
NA
Market rate
6,250 GBP–9,583 GBP/mo (75,000 GBP–114,996 GBP/yr)
Based on similar roles (title + domain + location).
About the Company
We are deliberately looking for someone whose first training was scientific or clinical, not computational. If you know why a particular Phase II endpoint gets chosen, what makes an inclusion criterion unworkable at site level, or how messy real-world data actually is when you try to use it - and you have since started teaching yourself to build with Python, ML and LLMs - you are exactly who we want to hear from. This is a development role. We will invest in your engineering craft, and you'll be supported by experienced ML and platform engineers. What we can't build as quickly is deep domain intuition, so that's what we're hiring for. We'd rather have a clinician or scientist who is three projects into their AI journey than an engineer who has never sat in a protocol review. If you're early in your career - recently out of a PhD or a doctoral training programme in AI for health, drug discovery or health data science, or making your first move out of clinical practice or academic research - you are welcome here.
Requirements
- ● Domain knowledge - familiarity with drug development, clinical trial design, or real-world data (EHR, claims, prescriptions)
- ● Ph.D. or equivalent professional experience in a health or life science field (medicine, pharmacy, pharmacology, epidemiology, biostatistics, immunology, neuroscience, translational or clinical research, or bioinformatics).
- ● Previous industry experience building applied ML/AI systems that have shipped as part of a product and driven measurable business impact.
- ● Machine Learning & AI
- ● Hands-on work with generative AI - including prompt engineering, context engineering and multiagent systems and working with managed endpoints (OpenAI, Anthropic, AWS Bedrock) and open-weight models (Hugging Face ecosystem)
- ● Knowledge of agentic design patterns and working with LLMs
- ● Scientific rigour applied to AI. You ask if what is outputted even makes sense.
- ● Engineering & Delivery
- ● Python development skills
- ● Awareness of working cloud environments
- ● Communication & Collaboration
- ● Ability to translate complex technical work into clear narratives for both technical and non-technical stakeholders
- ● Nice to Have (Desirable Requirements)
- ● ML - Experience of classical ML and NLP methods
- ● Software craft - testing, observability, documentation, code review
- ● RAG pipelines at depth - experience building secure, compliant ingestion and retrieval systems with provenance tracking, including web automation, parsing, and document processing
- ● Agent frameworks - hands-on experience with multi-agent orchestration tools (e.g., Google ADK, StrandsAgents, LangGraph, CrewAI, or equivalents)
- ● Real world data sources - HER, claims, prescriptions, registries - and their pitfalls.
- ● AI-augmented development - effective use of agentic coding assistants (Copilot, Cursor, Claude Code) to accelerate delivery
- ● Startup-pace experience - comfort with ambiguity, rapid iteration, and wearing multiple hats
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