Senior Forward Deployed Engineer (Sr ML Engineer)
Career Category
Information SystemsJob Description
CAREER LEVEL: GCF 5 - Specialist
CAREER TRACK: Individual Contributor
PRIMARY SCOPE: End-to-end technical delivery ownership of a small AI or automation solution or substantial deployment 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 Forward Deployed 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 lifecycle from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact.You willbe part of AI Studioleadingthe technical delivery of complex AI and automation solutions throughdiscovery, solution design, build, evaluation, production deployment, early stabilization and measurable value,production deployment, earlystabilizationand measurable value.
You willmaintaintechnical continuity across the lifecycle, working with business stakeholders and multidisciplinary teams to shape the simplestviablesolution, coordinate execution, make delivery trade-offs, removeblockersand contribute hands-on to critical components. The role combines enterprise solution engineering, applied AI/ML, GenAI, RAG and agents, integration, evaluation,MLOps/LLMOps, security,governanceand production operations. Technical accountability complements, but does not replace, explicit product, business,complianceand long-term support ownership.
Roles & Responsibilities:
Lead discovery by clarifying the business workflow, users, intended outcome, value hypothesis, acceptance criteria, operational constraints, data readiness, integrationdependenciesand production implications;
Translatecomplexproblems into an executable solution design, delivery plan, technical workstreams, estimates, milestones, dependencies, risks, acceptance criteria, releaseapproachand support transition.
Build, prototype, review or contribute to critical production components to prove feasibility or unblock delivery, including AI-enabled applications, RAG, bounded agents, intelligent automation,APIsand integrations.
Define andmaintainthe integrated architecture across applications, workflows, data and knowledge pipelines, models, retrieval, agents, APIs, enterprise integrations, identity, access controls,observabilityand human review.
Orchestrate delivery across full-stack engineering, data science, ML and context engineering, testing, platform, security,complianceand business roles;
Establish integrated testing, AI evaluation and governance covering functional, performance, security, data, model, retrieval, generation, tool-use, human-oversightand operational behaviour with explicit release thresholds.
Coordinate production readiness through CI/CD, staged release, monitoring, logging, SLOs, rollback, recovery,runbooksand controlled deployment; support early issue triage,stabilizationand transition to the operating owner.
Communicate evidence, risks,trade-offsand status clearly; measure adoption and value and convert delivery lessons into reusable components, accelerators, standards,documentationand playbooks.
Basic Qualifications and Experience:
- Bachelor’s/Master’sdegree with 8 - 13years of experience in Computer Science,ITor related field.
Functional Skills:
Technical discovery, and value framing: Workflow analysis, intended-use definition, feasibility assessment, data and integration readiness, success measures, estimates, dependencymappingand technical go/no-go recommendations.
Enterprise solution architecture and integration: End-to-end design across applications, APIs, services, data and knowledge flows, models, retrieval, agents, workflows, persistence, identity, security zones, enterprisesystemsand support boundaries.
Applied AI/ML and GenAI engineering: Production Python and SQL; classical ML and NLP awareness; foundation-model integration, prompt and context management, RAG, structured output, provenance, citations, bounded tool use, permissions,recoveryand human control.
Evaluation,qualityand regulated delivery: Representative evidence, baselines, gold sets, error taxonomies, expert adjudication, model and retrieval quality, task success, safety, latency, reliability, failure analysis, Responsible AI, privacy, validation,auditabilityandGxPcontrols.
Cloud,DevSecOpsand lifecycle operations: Cloud-native services, containers, CI/CD, infrastructure as code, versioning, observability, SLOs, staged release, rollback, incidents, disaster recovery, capacity, FinOps, runbooks andMLOps/LLMOps.
Must-Have Skills:
Demonstrated end-to-end technical ownership of at least one production AI, ML, software,dataor automation solution that delivered a measurable enterprise outcome.
Strong hands-onproficiencyin Python and SQL, with experience designing or reviewing production software, APIs, services, data flows, evaluationpipelinesand enterprise integrations.
Proven ability to turn complex business problems into coherent technical designs, executable delivery plans, acceptancecriteriaand production-readiness evidence while coordinating multidisciplinary teams.
Advanced capability in at least one role-defining pillar-Applied AI/ML, GenAI/RAG/agents, full-stack and integration engineering, or AI platform/MLOps-plus credible breadth across the production lifecycle.
Good-to-Have Skills:
Advanced RAG,knowledgeand agent systems: Hybrid or graph retrieval, knowledge graphs, source verification, MCP-style integration, durable or multi-agent workflows, policyenforcementand adversarial testing.
Cloud,dataand AI platforms: AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless or event-driven systems, infrastructure as code,MLflow, Airflow, Kubeflow, observability and FinOps.
Full-stack,workflowand automation breadth: JavaScript or TypeScript, modern web applications, API gateways, distributed workflows, process automation, document orvision capabilities and human-AI review experiences.
Regulated delivery and capability building: Life sciences, biotechnology, pharmaceutical, healthcare,GxPorvalidated-system experience; reusable frameworks, accelerators, standards, platformcapabilitiesand mentoring.
Soft Skills:
Excellent critical thinking and ability to create clarity,structureand forward momentum in ambiguous situations.
Strong technical leadership through influence, credibility, constructivechallengeand hands-on problem solving.
Clear communication of evidence, uncertainty, risks, trade-offs,limitationsand delivery status to diverse audiences.
Sound judgment, ownership and resilience when balancing value, speed, quality, security, compliance, cost,maintainabilityand supportability across global teams.
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.
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