Forward Deployment Engineer

Our mission:
Axtria is building the pharmaceutical industry's largest Forward Deployment Engineering workforce — 1,000 certified FDEs embedded across the world's leading pharma and life sciences organizations. We are hiring across multiple levels. If you have 10 or more years of engineering, data, and AI experience in pharma or life sciences — and you have shipped production AI, not just built PoCs — we want to talk to you.

An Axtria Forward Deployment Engineer does not advise from the outside. They deploy forward — directly into a pharma client's environment, operating on the client’s data, within the client’s commercial or clinical operations — and build production AI systems that real teams use. The role demands three capabilities simultaneously: the technical depth to architect and build production AI end-to-end, the pharma domain knowledge to understand why the system needs to work the way it does, and the consulting maturity to operate independently at the client without needing to be managed.

This is not a proof-of-concept role. FDEs scope, build, and ship. The seniority of your level determines the scale and complexity of the engagement you lead; the core mandate — production AI in the client environment — is the same across all levels.

• Embed directly within pharma client organizations — operating as a trusted technical peer, not a vendor — and own the design, build, and deployment of production AI systems end-to-end on the client’s own infrastructure.
• Lead the technical workstream and direct teams of engineers — Agent Pipeline Engineers and AI-Augmented Engineers — under your architecture and delivery ownership.
• Hold the technical client relationship at Director, VP, and CDO level: scoping problems, presenting architecture trade-offs, defending design decisions under scrutiny, and translating technical outcomes into business language

• Architect multi-agent AI systems for pharma environments — spanning orchestration patterns, tool and function integration, retrieval-augmented generation, memory architectures, human-in-the-loop design, evaluation pipelines, and production MLOps.
• Build on the technology stacks clients already operate:
  Databricks (Delta Lake, Mosaic AI, Genie), Snowflake (Snowpark, Cortex AI, Cortex Analyst), AWS (Bedrock Agents, Sage Maker), and the Claude and Anthropic API stack with Model Context Protocol
• Design and implement AI evaluation frameworks appropriate for regulated pharma environments — probabilistic quality thresholds, RAGAS, LLM-as-judge, adversarial red-teaming, and audit-trail-compliant output governance.
• Ensure systems are production-grade: observable, maintainable, secure, and compliant with pharma data governance requirements including HIPAA, GDPR, and applicable FDA AI/ML guidance.
• Stay current with the agentic AI ecosystem — frameworks, model capabilities, evaluation techniques, and orchestration protocols — and translate emerging capability into deployment-relevant technical decisions

• Translate pharma commercial and clinical business problems into AI-solvable architectures without requiring a domain primer from the client — the depth of your domain expertise is part of what you bring to the engagement.
• Validate that AI outputs are accurate against pharma business logic and commercial or clinical norms — not just technically correct but domain-defensible and explainable to the end users who act on them.
• Serve as the connective layer between the client’s business problem and the technical solution, eliminating the scoping ambiguity that causes most pharma AI deployments to stall before production

• Contribute to Axtria’s growing library of pharma AI deployment patterns, reference architectures, and reusable accelerators — ensuring each client engagement adds to the institutional knowledge base rather than staying isolated in a single account.
• Identify opportunities to extend scope within existing client engagements by recognising where additional AI work streams could unlock further commercial or clinical value.
• Me…