# Senior Consultant, AI/ML Engineering at AstraZeneca

AstraZeneca is hiring a Senior Consultant, AI/ML Engineering in Bengaluru, India. Level rates it Builds AI ●●●●; you can [apply on AstraZeneca](https://jobsbylevel.com/go/0311f90a-04a9-41ed-ae0c-64723b1d0fad?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. India - Bangalore.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 90 out of 100)
- Location: India - Bangalore
- Posted: November 13, 2026
- Apply: https://jobsbylevel.com/go/0311f90a-04a9-41ed-ae0c-64723b1d0fad?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-consultant-ai-ml-engineering-at-astrazeneca-7704d0

## Prepare for this job

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## Description

Job Title: Senior Consultant, AI/ML Engineering Location: Bangalore GCL: D3 Introduction to role: Are you ready to harness Generative AI and agentic systems to transform how therapies reach patients and sharpen commercial decision-making? As a Senior Consultant in AI/ML Engineering based in Bangalore, you will design, build and ship AI solutions that help clinicians, field teams and partners act faster and smarter—ultimately improving outcomes for millions. Do you want to lead end-to-end delivery, from problem framing and experimentation through evaluation, deployment and monitoring? You will work across multiple disease areas and brands, integrating diverse data sources to deliver robust tools. You will join a global community of data scientists, ML engineers and domain experts, providing technical leadership on high-impact projects while deepening your expertise in the latest AI methods. Accountabilities: Generative and Agentic Solutions: Design, build and deploy Generative AI and agentic solutions for commercial use cases, including LLM applications and multi-agent systems on frameworks such as Amazon Bedrock AgentCore. Fine-tuning and Distillation: Develop and apply fine-tuning and distillation techniques to enhance quality while reducing cost and latency. Evaluation and Safety: Build and operate evaluation harnesses and platforms to measure quality, safety and reliability of LLM and agent systems, using results to drive continuous improvements. Production Telemetry and Monitoring: Implement telemetry, tracing and monitoring for AI systems in production and respond to quality, drift and cost issues. Reinforcement Learning: Explore and apply reinforcement learning and feedback-based optimisation where they add measurable value. Classical ML Delivery: Develop and maintain traditional ML models (classification, regression, clustering, forecasting, NLP) for commercial analytics. Data Integration: Integrate internal and external structured and unstructured data by partnering with data engineering, informatics and platform teams. Technical Leadership and Code Quality: Lead technical workstreams and write production-quality, well-tested and well-documented code that scales. Multi-functional Partnerships: Collaborate with Commercial, Medical, IT and other partners to scope problems, define success metrics and deliver scalable solutions. Research and Coaching: Research and apply the latest algorithms and methods to business problems, and coach junior data scientists and engineers to set the standard. Essential Skills/Experience: Minimum Requirements BS (or equivalent experience) in computer science, mathematics, statistics, engineering, physics, computational sciences or a related quantitative field. Typically 6+ years of experience building data science and ML solutions, including recent hands-on experience (typically 1+ years) with LLM-based applications. A track record of independently leading data science or AI projects to solve business or scientific problems. Mandatory Technical Skills Generative AI and LLMs: prompt and context engineering, retrieval-augmented generation, and working knowledge of fine-tuning and distillation approaches. Agentic AI: experience building agents or multi-agent workflows using frameworks such as Amazon Bedrock AgentCore, LangGraph, Strands or similar. Evaluation: experience designing and running evaluations of LLM and agent systems. Machine learning foundations: classical ML (supervised and unsupervised methods, including classification and regression) and deep learning, including transformers, using PyTorch or TensorFlow. Programming and tools: exceptional Python; SQL and familiarity with NoSQL; scikit-learn; Jupyter; Streamlit or similar. Cloud: working knowledge of AWS (SageMaker, Bedrock preferred). Desirable Skills/Experience: Master’s or PhD in machine learning, computer science or a related field. We prioritise demonstrated skills over credentials. Familiarity with reinforcement learning,

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