AstraZeneca【AstraZeneca】【CET】Agentic AI Lead,Cutomer Experience & IT
AstraZeneca is hiring a 【AstraZeneca】【CET】Agentic AI Lead,Cutomer Experience & IT for a remote role open to applicants in Japan. Level rates it ; you can apply on AstraZeneca.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include OpenAI, Claude and LangGraph.
- Level classifies it as a lead role.
- Level lists 1,300+ other open roles at AstraZeneca.
AI in this role
Lead the design, building, and evaluation of GenAI and agentic AI production systems for commercial and medical operations.
Job Description
The Agentic AI Lead is the senior hands-on engineer in the Enterprise AI Technology Delivery team. The role designs, builds, evaluates and operates GenAI and agentic AI systems that AstraZeneca Japan's commercial, medical and field organizations rely on in production, and raises the engineering practice of the team that builds them. This is a building role: the majority of the Lead's time is spent in code, traces and evaluation data, not in coordination.
The Lead takes ownership of the team's existing AI platform assets and key systems for business users. The Lead is also accountable for their reliability, cost and security. The Lead works autonomously with limited supervision, provides technical direction and coaching to the team's AI engineers, and partners with business owners to make sure what gets built solves a validated business problem.
Experience
Mandatory
1. 5+ years building and running software in production (Python; AWS or equivalent cloud).
2. 2+ years hands-on shipping LLM-based or agentic applications to real users, not only proofs of concept.
3. Evaluation-driven practice: can show an evaluation set, error analysis and the iteration it drove on a real project.
4. Daily use of agentic coding tools (Claude Code, Codex or equivalent) as a core part of engineering work, including reviewing and testing their output.
5. Experience leading a technical workstream or mentoring engineers (formal team-lead experience not required; this is a hands-on role, ~80% coding / 20% coordination, suited to a strong developer ready to grow into a lead).
Nice to have
Trained, evaluated or maintained supervised or deep-learning models in production.
Experience in pharma or another regulated industry .
Experience co-developing with external delivery partners or vendors.
Front-end development (React/Next.js) for agent user interfaces.
License
Mandatory
None. Bachelor's degree in Computer Science or a related quantitative discipline, or equivalent experience.
Nice to have
AWS certification (Solutions Architect Associate or Developer Associate, or higher).
Master's or PhD in Computer Science or a related quantitative discipline.
DeepLearning.AI or equivalent LLM/agentic AI coursework.
Skill-set
Mandatory
Agent architecture: workflow vs. agent harness, tool calling, MCP, memory and context management, guardrails against prompt injection and data exfiltration.
At least one agent framework ( Bedrock AgentCore, LangGraph, OpenAI Agents SDK, or equivalent).
Grounding: RAG with vector stores and retrieval over structured data via SQL.
LLM foundations: model selection, tokenisation and cost, context window and caching, when to fine-tune or self-host.
Production operations for AI systems: observability and tracing, cost/latency optimisation, statistical regression testing in CI/CD, prompt/model versioning, infrastructure-as-code.
Software engineering fundamentals: full-stack application design, data modelling, testing strategy, secure-by-design practice (dependency scanning, secrets hygiene).
AWS in production: Lambda, ECS/Fargate, API Gateway, Bedrock, IAM.
Clear written and verbal explanation of technical trade-offs and uncertainty to non-specialists.
Nice to have
Knowledge graphs or semantic layers over structured data (e.g. Snowflake).
Document-processing pipelines for Japanese-language material.
Customising agent environments: skills, MCP servers, hooks, shared context files (CLAUDE.md/AGENTS.md).
Eval/tracing tooling such as Langfuse, Braintrust or LangSmith.
Azure OpenAI and multi-provider gateway experience.
Languages
Mandatory
Japanese：Business level. For non-native speakers: JLPT N2 or higher (N1 preferred).
Native-level Japanese for business stakeholdesr
English：Business level (technical documentation, working with global AZ teams).
Date Posted
17-9月-2026Closing Date
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
How we rate this
【AstraZeneca】【CET】Agentic AI Lead,Cutomer Experience & IT at AstraZeneca rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Tell me about a project where genai was part of your work. What did you do?
- Tell me about a project where agentic ai was part of your work. What did you do?
- Tell me about a project where llms was part of your work. What did you do?
- How do you structure and test a prompt to get consistent output from a language model?
Adapt your resume
- List these exact terms on your resume: RAG, GenAI, Agentic AI, LLMs, and Prompt engineering. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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