Machine Learning Engineer, AI Studio

India - Hyderabad

JOB ID: R-251201 LOCATION: India - Hyderabad WORK LOCATION TYPE: On Site DATE POSTED: Aug. 05, 2026 CATEGORY: Information Systems

Machine Learning Engineer, AI Studio

CAREER LEVEL:GCF 4 – Senior Associate

CAREER TRACK:Individual Contributor

PRIMARY SCOPE:Independent ownership of defined production ML/AI components

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:

TheMachine Learning Engineeroffers 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 lifecyclefrom early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact.You will independently own defined production components within enterprise AI products and automation solutions.

Your remit may include a model or inference service, data or knowledge pipeline, retrievalcomponent, agent tool, evaluation module, API,workflowor monitoring capability. You will design, release, diagnose and support thecomponentwhile connecting technical measures to user and workflow outcomes. Within Applied AI, AI Studio turns 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:

• Definecomponentboundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations and technical estimates with product and architecture partners.

• Design and implement maintainable Python, SQL, API, data, model, retrieval, agent-tool and workflow components with clear contracts, configuration, testing, errorhandlingand documentation.

• Apply EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup, threshold, explainability and error analysis where relevant.

• Build GenAI, NLP, RAG and bounded agent components using structured output, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries and recoverable failurebehavior.

• Engineer batch or event-driven data, document, feature, embedding, label and evaluation pipelines with schema validation, lineage, provenance, accesscontroland consistency checks.

• Define representative evaluation for model quality, uncertainty, retrieval, grounding, citations, task success, tool correctness, safety, latency,costand user impact.

• Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback, incidentresponseand runbooks; lead diagnosis of moderately complex failures.

• Apply security, privacy, Responsible AI, validation, auditability, human oversight and applicableGxPcontrols; contribute reusable assets and guide Associate engineers on familiar work.

Basic Qualifications and Experience:

• Bachelor’s/Master’sdegree and 5 to 9 years of Computer Science,ITor related field experience.

Functional Skills:

• Production software and AI/ML system design: Python and SQL modules, APIs, background jobs, event flows, testing, performance, observability, sourcecontroland maintainable failure semantics.

• Statistics,modelingand experimentation: EDA, feature engineering, classification, regression, clustering, ensembles, cross-validation, leakage prevention, calibration,uncertaintyand decision-aware error analysis.

• GenAI, RAG,knowledgeand agents: Prompt and context management, structured output, chunking, embeddings, hybrid retrieval, reranking, citations, access-aware retr…