{"id":3958722,"url":"https://alion.io/job/astrazeneca-director-aiml","title":"Director, AI/ML","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":94,"open_postings":158,"ghost_share":0.006,"stale_share":0.38,"repost_share":0.108,"time_to_fill_p50_days":19,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"TensorFlow","optional":false},{"name":"Knowledge Graph","optional":true},{"name":"LangChain","optional":true},{"name":"NLP","optional":true},{"name":"RLAIF","optional":true},{"name":"RLHF","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-09T10:23:02Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T10:23:02Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Title: DIRECTOR, AI/ML\nGCL: F\nLocation: Bangalore\nIntroduction to role:\nAre you ready to set the technical bar for AI systems that shape how life-changing therapies reach patients? In this senior individual-contributor role based in Bangalore, you will be the technical authority guiding advanced AI across our commercial landscape-from traditional ML to Generative and agentic AI-turning breakthroughs into measurable outcomes for patients and the business.\nYou will lead through influence, not headcount, defining architecture, standards and strategy while pairing that with hands-on expertise. Partnering with colleagues across Commercial, Medical and Technology, you will help transform diverse data-patient, physician, health systems, sales and scientific content-into rigorous, scalable AI that drives better decisions at pace. How would you design resilient AI systems that are trusted, governed and production-ready from day one?\nAccountabilities:\nTechnical Vision and Architecture: Define and drive the technical vision, roadmap and architecture for traditional ML, Generative AI and agentic AI capabilities, aligned to enterprise strategy and commercial priorities.\nMulti-functional Leadership: Provide hands-on technical leadership to project teams; set design direction, review architectures and code, and remove blockers on sophisticated problems.\nAgentic AI Systems: Lead the design of multi-agent systems and LLM applications, including orchestration, tool use, memory, guardrails and human-in-the-loop patterns.\nEvaluation Excellence: Establish standards for evaluation, including automated and human-in-the-loop methods, benchmarking, and quality gates pre- and post-release.\nModel Optimization: Drive approaches to fine-tuning, distillation and reinforcement learning (including RL from feedback) to improve quality, cost and latency over off-the-shelf models.\nProduction Observability: Set standards for telemetry, observability, monitoring, drift detection and cost management for AI systems in production.\nEmerging Tech to Production: Evaluate emerging models, frameworks and methods; run proofs of concept and translate them into robust, production-grade solutions.\nEnterprise Alignment: Partner with platform, data engineering, architecture, security and responsible-AI teams to ensure governance, compliance and reuse.\nPartner Communication: Translate complex technical concepts and trade-offs for senior non-technical partners and shape compelling use cases for AI investments.\nTalent Uplift and Representation: Mentor senior data scientists and ML engineers, raise the technical bar, and represent our work in internal forums and partner engagements.\nEssential Skills/Experience:\nMinimum Requirements:\nBS (or equivalent experience) in computer science, mathematics, statistics, engineering, physics, computational sciences or a related quantitative field.\nExtensive experience (typically 12+ years) building and deploying data science and ML solutions, including considerable recent hands-on experience (typically 2+ years) with LLM-based and agentic applications in production.\nDemonstrated track record of setting technical direction and leading complex, multi-functional AI delivery through influence.\nMandatory Technical Skills:\nGenerative AI and LLMs: prompt and context engineering, retrieval-augmented generation, fine-tuning (including parameter-efficient methods), distillation, and model selection and cost/latency optimisation.\nAgentic AI: design and delivery of multi-agent systems using frameworks such as Amazon Bedrock AgentCore (or equivalents such as LangGraph, Strands or AutoGen), including tool integration, orchestration and guardrails.\nEvaluation and observability: LLM and agent evaluation methods and platforms, telemetry, tracing and monitoring of AI systems.\nMachine learning foundations: strong classical ML (supervised and unsupervised methods, statistical modelling) and deep learning, including transformers, using PyTorch or TensorFlow.\nProgramming and tools: expert Python; SQL; scikit-learn; Jupyter; Streamlit or similar for rapid prototyping.\nCloud and MLOps: AWS (SageMaker, Bedrock), CI/CD for ML, MLflow or equivalent experiment and model tracking, containerisation (Docker).\nDesirable Skills/Experience:\nMaster’s or PhD in machine learning, computer science or a related field. We prioritise demonstrated skills over credentials.\nHands-on experience with reinforcement learning, including RLHF, RLAIF or other preference-optimisation methods.\nExperience with knowledge graphs, NLP, and structured and unstructured data (text and image).\nExperience with data and pipeline tooling such as Airflow, Spark and data-quality frameworks.\nPharmaceutical, healthcare or commercial analytics domain experience (for example next-best-action, field-force, marketing or market-access analytics).\nPublications, patents or open-source contributions in AI/ML.\nExceptional technical judgement, balancing innovation with pragmatism, risk and cost.\nStrong written and verbal communication, explaining complex methods to non-technical audiences.\nCollaborative approach to working with global, multi-functional teams.\nCuriosity and an aim to keep learning in a fast-moving field.\nWhen we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.\nWhy AstraZeneca:\nHere, your technical leadership meets meaningful scale: we build from the ground up as one team, connecting across the enterprise to convert complex data into practical insights that improve lives. You will collaborate with deep specialists and external partners, access cutting-edge tooling, and contribute to novel solutions that move from prototype to production with rigor and pace. We value ambition and kindness in equal measure, pair bold experimentation with strong ethics, and bring unexpected teams together to unlock ideas that drive real patient impact-while giving you the room to grow your craft and your profile.\nCall to Action:\nIf you are ready to define the standards for AI that powers real-world decisions and deliver impact at scale, step forward and help us build what’s next!\nDate Posted\n09-Nov-2026Closing Date\nAstraZeneca 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. 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