Sr Data Scientist / AI Applications Engineer

India - Hyderabad

JOB ID: R-252407 LOCATION: India - Hyderabad WORK LOCATION TYPE: On Site DATE POSTED: Aug. 07, 2026 CATEGORY: Process Development

Job Summary

Transformative Digital Capabilities (TDC) drives Process Development (PD) and Operations digital transformation through pragmatic digital innovation that systematically delivers for the present while also building for the future. Our mission is to empower Process Development staff to harness their transformative potential through the rapid convergence of chemistry, biology, engineering, and computing, accelerated by innovative digital capabilities. TDC’s core competencies span clear thought leadership and deep domain expertise in Data Products, Artificial Intelligence, Digital Twins, Product Management, and Organizational Change Management following Scaled Agile practices.

As part of our team expansion at Amgen India (AIN), we are seeking a multi-faceted Level 5 AI Engineer to design, develop, evaluate, and deploy AI-enabled workflow capabilities for Process Development. The successful candidate will translate ambiguous scientific and operational needs into executable technical work, including agents, context engineering patterns, workflow automation, retrieval and data pipelines, evaluation harnesses, benchmarks, and evidence packs. This role will partner closely with U.S. TDC leaders, AIN colleagues, Rapid AI squads, data strategy teams, experimental data teams, and software/platform partners. The role requires strong technical ownership, clear communication, and disciplined execution across time zones.

What You Will Do

• Frame and deliver AI-enabled use cases: translate ambiguous business, scientific, engineering, and operational needs into actionable product, data, and AI use cases, then design and implement practical workflow solutions that solve real Process Development problems.
• Build workflow, data, and knowledge foundations: develop agents, automation patterns, retrieval flows, context engineering approaches, and human-in-the-loop workflows, while working with structured and unstructured experimental data, metadata, protocols, reports, controlled vocabularies, taxonomies, semantic models, entity relationships, graph data models, entity resolution approaches, and semantic query patterns.
• Support product ownership and adoption: work with product owners and business partners to shape backlog, prioritization, release planning, stakeholder expectations, feedback loops, adoption approach, and measurable client outcomes for AI-enabled capabilities.
• Provide technical leadership in delivery: help data scientists, AI engineers, ontology specialists, product owners, business partners, and other stakeholders work through delivery decisions, dependencies, and tradeoffs without adding people-management scope to the role.
• Develop evaluation, evidence, and reusable solutions: create benchmarks, test sets, acceptance criteria, evaluation harnesses, quality checks, and evidence packs, and package prompts, retrieval logic, data contracts, notebooks, scripts, or services so solutions can be reused rather than remaining one-off prototypes.
• Document and communicate the technical basis: explain assumptions, data lineage, model or agent behavior, limitations, validation evidence, and appropriate use to technical and non-technical stakeholders.

Basic Qualifications

• Bachelor’s OR Master’s degree and minimum of 7 years of related experience in engineering, computer science, data science, applied mathematics, statistics, life sciences, or a related technical field

Required Skills:

• Hands-on experience building AI, machine learning, generative AI, agentic, retrieval-augmented generation, workflow automation, or decision-support systems.
• Strong programming skills in Python and experience with modern software engineering practices, including version control, testing, packaging, documentation, and reproducible workflows.
• Experience with experimental data, scientific metadata, laboratory or process development workflows, knowledge management, data pipelines, or data product development.
• Experience designing evaluation methods for AI or analytics systems, including benchmarks, test sets, model or agent performance metrics, error analysis, and acceptance criteria.
• Ability to translate ambiguous scientific or operational problems into clear technical requirements, implementation plans, and measurable outcomes.
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