Senior Forward Deployed Engineer, AI Studio

Amgen

Confirmed live 2 days ago High trust
Remote

Quick summary

Work type
Remote
Location
Jacksonville, FL
Salary
$156,190–$211,315 / yr
Posted
37 days ago
Freshness
Confirmed live 2 days ago
Nearby
62 roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $204k
This role $184k
$145k most similar roles pay here $265k

This role pays less than 63% of similar roles. Most pay $162,150–$246,150 — the shaded band above. At the midpoint, this role pays about $184k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Amgen

Amgen is a leading biotechnology company that discovers, develops, manufactures, and delivers innovative human therapeutics, with products addressing conditions such as anemia, bone loss, inflammation, and cancer. Industry: Biotechnology & Pharmaceuticals

Amgen currently has 46 open roles on FindRole.

Listed pay typically runs $136,906–$184,235 across 44 roles with salary data.

Most-posted roles

View all roles at Amgen

At a glance

TL;DR · Senior Forward Deployed Engineer, AI Studio

Role Posting Log in to save

The Senior Forward Deployed Engineer, AI Studio joins the AI & Data Science organization to lead the technical delivery of complex AI and automation solutions. This role involves managing the full lifecycle from discovery and solution design to production deployment and stabilization. You will translate business requirements into executable plans, build prototypes for RAG, bounded agents, and intelligent automation, and maintain integrated architectures across data pipelines and enterprise systems. Key responsibilities include coordinating multi-disciplinary teams, ensuring MLOps/LLMOps standards, and managing security and governance. The role requires proficiency in Python, SQL, and cloud-native technologies like AWS, Bedrock, SageMaker, Databricks, and Kubernetes. You will solve technical challenges within the biotechnology and pharmaceutical sectors, specifically navigating regulated environments to deliver high-value AI applications that improve operational efficiency and product quality while ensuring compliance with GxP controls.

What does a Forward Deployed Engineer earn?

Median $215000 from 45 postings across 23 companies.

See salary data

Skills

Python SQL GenAI RAG MLOps LLMOps AWS Bedrock SageMaker Databricks Spark Kubernetes CI/CD Infrastructure as Code MLflow Airflow Kubeflow TypeScript JavaScript API NLP

What you'll do

  • Lead discovery by clarifying business workflows, user requirements, value hypotheses, and operational constraints for AI solutions.
  • Translate complex problems into executable solution designs, including technical workstreams, delivery plans, and risk assessments.
  • Build and prototype critical production components such as RAG systems, bounded agents, and intelligent automation tools.
  • Define and maintain integrated architecture across data pipelines, models, retrieval systems, and enterprise integrations.
  • Establish comprehensive testing frameworks for AI evaluation, security, performance, and regulatory compliance.
  • Coordinate production readiness through CI/CD pipelines, monitoring, logging, and automated deployment protocols.
  • Communicate technical evidence, risks, and trade-offs clearly to both technical and non-technical stakeholders.
  • Convert delivery lessons into reusable components, standard playbooks, and documentation for the organization.

What we're looking for

  • A Doctorate degree with 1 year of experience in Computer Science, IT, or a related field.
  • A Master’s degree with 8 to 10 years of experience in Computer Science, IT, or a related field.
  • A Bachelor’s degree with 10 to 12 years of experience in Computer Science, IT, or a related field.
  • A Diploma with 12 to 14 years of experience in Computer Science, IT, or a related field.
  • Proficiency in Python and SQL for developing production software, APIs, data flows, and evaluation pipelines.
  • Experience in applied AI/ML, GenAI, RAG, and MLOps/LLMOps within a production environment.
  • Expertise in cloud-native services, CI/CD, infrastructure as code, and containerized environments like Kubernetes.
  • Experience in regulated industries such as life sciences, biotechnology, or pharmaceutical manufacturing (GxP).

Related searches

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