Senior Forward-Deployed AI Studio Engineer (Remote)

Vibehackers

United States

On-site

USD 170,000 - 210,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

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.