Sr Machine Learning Engineer, AI Studio

Career Category

Information Systems

Job Description

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 or   substantial   technical 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,   fuller   and longer. We discover, develop,   manufacture   and deliver innovative medicines to help millions of patients. Amgen helped   establish   the biotechnology industry more than 40 years ago and   remains   at the   cutting edge   of innovation, using technology and human genetic data to push beyond what is known today.

ABOUT THE ROLE

Role Description:

The Senior Machine Learning Engineer position   offers 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 to   identify   the 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.   Y ou will   be   part of AI Studio and   define and own AI asset s   or substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition,   adoption   and 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,   governance   and 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,   observability   and lifecycle operations.

Roles & Responsibilities:

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

Map rules, exceptions, data   dependencies   and human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG,   agents   or a manual approach.

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

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

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

Establish   MLOps / LLMOps   for lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity,   cost   and runbooks.

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

Basic Qualifications and Experience:

•  Bachelor’s/ Master’s   degree with 8 - 13 years of experience in Computer Science,   IT   or 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, design   review   and end-to-end architecture.

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

Deep learning,   NLP   and foundation models: Selection and production use of neural, transformer, embedding, vision,   document   and multimodal approaches, including fine-tuning versus prompting, latency,   privacy   and cost trade-offs.

GenAI, RAG,   knowledge   and agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarial   evaluation   and human control.

Data, knowledge,   cloud   and AI operations:   Trustworthy   batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes,   MLOps / LLMOps , SLOs and lifecycle operations.

Responsible AI and regulated delivery: Risk assessment, least privilege, threat   modelling , red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicable   GxP   controls.

Must-Have Skills:

Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome.

Strong hands-on   proficiency   in Python and SQL, with experience designing production software,   services   and evaluation pipelines.

Advance   capability in at least one role-defining pillar, Applied ML, GenAI/RAG/ agents   or ML platform/ MLOps, plus credible depth across the production lifecycle.

Good-to-Have Skills:

Advanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival   methods   or drift-aware retraining.

Advanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or   LoRA , fine-tuning, distillation, quantization, routing,   cascades   or inference optimization.

Cloud,   platform   and AI operations: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code,   MLflow , Airflow, Kubeflow, observability and FinOps.

Human-AI and regulated delivery: Experience with review, correction, approval, accessibility, uncertainty communication, workflow automation and   GxP -relevant or validated systems.

Soft Skills:

Strong product thinking and ability to connect technical decisions to user, workflow, risk,   cost   and business value.

Technical leadership, mentoring and constructive challenge while   remaining   hands-on.

Excellent analytical judgment and clear communication of evidence, uncertainty,   trade-offs   and limitations.

Cross-functional leadership across business, product, architecture,   engineering   and control functions.

Ownership, resilience and continuous improvement through incidents,   feedback   and measured outcomes.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race,   colour , religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.

.