Sr Machine Learning Engineer, AI Studio

India - Hyderabad

JOB ID: R-251199 LOCATION: India - Hyderabad WORK LOCATION TYPE: On Site DATE POSTED: Aug. 05, 2026 CATEGORY: Information Systems

Senior Machine Learning Engineer, AI Studio

CAREER LEVEL:GCF 5 – Specialist

CAREER TRACK:Individual Contributor

PRIMARY SCOPE:End-to-end ownership of a small AI asset orsubstantialtechnical workstream

ORGANIZATION:Applied AI AI Studio

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier,fullerand longer. We discover, develop,manufactureand deliver innovative medicines to help millions of patients. Amgen helpedestablishthe biotechnology industry more than 40 years ago andremainsat thecutting edgeof innovation, using technology and human genetic data to push beyond what is known today.

ABOUT THE ROLE

Role Description:

The Senior Machine Learning Engineer positionoffers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) - organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company toidentifythe right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle—from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact.You willbepart of AI Studio anddefine and own AI assetsor substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition,adoptionand measurable outcome.

You will remain hands-on while leading decisions across software, statistics, classical ML, deep learning, NLP, GenAI, RAG, bounded agents, data and knowledge pipelines, APIs,MLOps/LLMOps, security,governanceand operations. Within Applied AI-AI Studio turn prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation,observabilityand lifecycle operations.

Roles & Responsibilities:

• Define the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operatingownerand measurable technical and business outcomes.

• Map rules, exceptions, datadependenciesand human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG,agentsor a manual approach.

• Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, securityzonesand human review.

• Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agenttoolsand workflow orchestration.

• Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expertadjudicationand release thresholds.

• EstablishMLOps/LLMOpsfor lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity,costand runbooks.

• Coordinate security, privacy, Responsible AI, Quality, legal, model-risk andGxPcontrols; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices.

Basic Qualifications and Experience:

• Bachelor’s/Master’sdegree with 8 - 13 years of experience in Computer Science,ITor related field.

Functional Skills:

• Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, designreviewand end-to-end architecture.

• Advanced statistics,MLand experimental design: EDA, feature engineering, supervised and unsupervised learning, predictivemodelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified.

• Deep learning,NLPand foundation models: Selection and production use of neural, transformer, embedding, vision,documentand multimodal approaches, including fine-tuning versus prompting, latency,p…