Senior Forward Deployed Engineer, AI Studio

Vibehackers; United States; On-site; USD 170,000 - 210,000; Full time

Benefits offered by this job

Retirement plan

Medical/dental/vision coverage

Stock-based incentives

Paid time-off plans

Flexible work models

Job summary

Amgen’s AI Studio seeks a Senior Forward Deployed Engineer to lead the delivery of complex AI and automation solutions, spanning discovery, design, build, evaluation, and production deployment for GenAI, RAG and agents with MLOps/LLMOps. You will coordinate multi-disciplinary teams, ensure governance and security, and drive measurable enterprise value.

The role emphasizes production-ready AI, integration across data and knowledge pipelines, and robust operational readiness including CI/CD,

Qualifications

• Advanced AI/ML and GenAI engineering: production Python and SQL; foundation-model integration, prompt and context management, RAG, structured output, provenance and human control.
• Evaluation, quality and regulated delivery: baselines, gold sets, error taxonomies, validation, Responsible AI, privacy, auditability and GxP controls.
• Cloud, DevSecOps and lifecycle operations: cloud-native services, containers, CI/CD, infrastructure as code, versioning, observability, SLOs, staged release, rollback and MLOps/LLMOps.
• Demonstrated end-to-end technical ownership of at least one production AI/ML/software/data/automation solution with measurable enterprise outcomes.

Responsibilities

• Lead discovery: clarify workflows, users, outcomes, value hypotheses, acceptance criteria, operational constraints, data readiness and integration dependencies.
• Translate problems into executable solution designs, delivery plans, workstreams, estimates, milestones, dependencies, risks and release approaches.
• Build, prototype or contribute to production components (AI-enabled apps, RAG, bounded agents, integrations, APIs) to prove feasibility and unblock delivery.
• Define and maintain integrated architecture across applications, workflows, data and knowledge pipelines, models, retrieval, agents, APIs, identity, access controls and observability.
• Orchestrate delivery across full-stack engineering, data science, ML/context engineering, testing, platform, security, compliance and business roles.
• Establish integrated testing, AI evaluation and governance covering functional, performance, security, data, model, retrieval, generation, tool-use and human oversight with release thresholds.
• Coordinate production readiness via CI/CD, staged release, monitoring, logging, SLOs, rollback, recovery and runbooks; support early triage and transition to operating owners.
• Communicate evidence, risks, trade-offs and status; measure adoption and value and convert lessons into reusable components, accelerators, standards and playbooks.

Skills

Python

SQL

GenAI

RAG

Agents

MLOps/LLMOps

Cloud platforms

APIs

Security & Governance

Leadership

Education

PhD in CS/IT or related

MS in CS/IT or related

Bachelor's in CS/IT

Diploma in related field

Tools

CI/CD

Kubernetes

Data pipelines

Observability tools

Job description

Uses GenAI, RAG and agent systems and emphasizes MLOps/LLMOps and production-ready AI — fits with building AI dev tools and production-focused rapid prototyping.

About the Role

Lead technical delivery of complex AI and automation solutions within Amgen’s AI Studio, driving discovery, solution design, prototyping, production deployment, and early stabilization for GenAI, RAG, agents and MLOps initiatives to deliver measurable enterprise value for patient-focused products.

Job Description

Role

Senior Forward Deployed Engineer on AI Studio responsible for leading technical delivery of complex AI and automation solutions through discovery, design, build, evaluation, production deployment and early stabilization. The role blends enterprise solution engineering, applied AI/ML (including GenAI, RAG and agents), integration, MLOps/LLMOps, security, governance and production operations while coordinating multidisciplinary teams and business stakeholders.

Key Responsibilities

• Lead discovery: clarify workflows, users, outcomes, value hypotheses, acceptance criteria, operational constraints, data readiness and integration dependencies.
• Translate problems into executable solution designs, delivery plans, workstreams, estimates, milestones, dependencies, risks and release approaches.
• Build, prototype or contribute to production components (AI-enabled apps, RAG, bounded agents, integrations, APIs) to prove feasibility and unblock delivery.
• Define and maintain integrated architecture across applications, workflows, data and knowledge pipelines, models, retrieval, agents, APIs, identity, access controls and observability.
• Orchestrate delivery across full-stack engineering, data science, ML/context engineering, testing, platform, security, compliance and business roles.
• Establish integrated testing, AI evaluation and governance covering functional, performance, security, data, model, retrieval, generation, tool-use and human oversight with release thresholds.
• Coordinate production readiness via CI/CD, staged release, monitoring, logging, SLOs, rollback, recovery and runbooks; support early triage and transition to operating owners.
• Communicate evidence, risks, trade-offs and status; measure adoption and value and convert lessons into reusable components, accelerators, standards and playbooks.

Requirements

Basic Qualifications

• Doctorate + 1 year experience in Computer Science/IT or related OR
• Master’s degree + 8-10 years experience OR
• Bachelor’s degree + 10-12 years experience OR
• Diploma + 12-14 years experience

Preferred Qualifications

• Technical discovery and value framing (workflow analysis, feasibility, data/integration readiness, success measures).
• Enterprise solution architecture and integration across applications, APIs, services, data and knowledge flows, models, retrieval and workflows.
• Applied AI/ML and GenAI engineering: production Python and SQL; foundation-model integration, prompt and context management, RAG, structured output, provenance and human control.
• Evaluation, quality and regulated delivery: baselines, gold sets, error taxonomies, validation, Responsible AI, privacy, auditability and GxP controls.
• Cloud, DevSecOps and lifecycle operations: cloud-native services, containers, CI/CD, infrastructure as code, versioning, observability, SLOs, staged release, rollback and MLOps/LLMOps.
• Demonstrated end-to-end technical ownership of at least one production AI/ML/software/data/automation solution with measurable enterprise outcomes.
• Strong hands-on proficiency in Python and SQL; experience designing or reviewing production software, APIs, services, data flows and evaluation pipelines.
• Advanced capability in a role-defining pillar (Applied AI/ML, GenAI/RAG/agents, full-stack integration engineering, or AI platform/MLOps) with credible lifecycle breadth.
• Advanced RAG, knowledge and agent systems experience (hybrid/graph retrieval, knowledge graphs, source verification, multi-agent workflows and adversarial testing).
• Familiarity with cloud, data and AI platforms and tools listed in preferred qualifications.
• Strong communication, technical leadership, critical thinking and ability to drive delivery in ambiguous situations.

Location & Employment Type

• Job Location: Washington D.C., District of Columbia, United States
• Location Type: Remote
• Contract Type: Full-time

Compensation & Benefits (summary)

• Posting indicates an expected annual salary range for U.S. applicants but no specific numbers are provided in the text.
• Benefits include retirement and savings plan with company contributions, medical/dental/vision coverage, life and disability insurance, flexible spending accounts, discretionary annual bonus, stock-based incentives, paid time-off plans and flexible work models.

Skills

Solution Architecture System Design Applied AI/ML Generative AI RAG and Agents MLOps/LLMOps Integration API Design Cloud Operations DevSecOps Testing & Evaluation Governance & Compliance Observability & Monitoring Production Deployment Stakeholder Management Technical Leadership Communication Problem Solving Prototyping / POC Data Engineering Security

Experience Level

Senior

Employment Type

Full Time

• Retirement and Savings Plan (company contributions)