Forward Deployment Engineer

Position Summary:

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

KEY RESPONSIBILITIES:

Embedded Client Delivery

• 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

AI Architecture and Engineering

• 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, SageMaker), 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

Pharma Domain Translation

• 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

Program Contribution and Methodology

• 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 workstreams could unlock further commercial or clinical value.
• Mentor junior FDEs and AI-Augmented Engineers within your engagement team, and contribute to the technical standards and delivery methodology of the FDE program.

REQUIRED QUALIFICATIONS:

• 10 or more years of experience in engineering, data engineering, analytics engineering, or AI/ML roles, with direct pharma or life sciences client exposure across a substantial portion of that tenure
• Pharma domain experience is mandatory. You understand how pharma organisations work without needing orientation: commercial depth in SFE, IC design, market access, omnichannel, KAM, or patient services; or clinical depth in trial operations, CDASH/SDTM, site analytics, RWE, HEOR, or regulatory data. You do not need a pharma primer to start this role.
• Production AI engineering is required — you have shipped, not just experimented. You have built AI systems that ran in production: LLM-based agents, RAG pipelines, multi-agent workflows, or clinical AI systems with real users in regulated environments. Notebooks, PoCs, and sandbox projects do not qualify for this requirement.
• Strong engineering foundations: Python, SQL, cloud infrastructure (AWS, Azure, or GCP), data pipeline architecture, and API design — you write production-grade code as a matter of course, not occasionally.
• Depth on at least one pharma AI platform — Databricks, Snowflake, AWS Bedrock, or the Claude and Anthropic API stack — and working fluency across the others sufficient to engage in architecture trade-off discussions.
• Demonstrated ability to operate in direct client-facing roles at senior stakeholder level: scoping ambiguous problems, managing technical expectations, presenting architecture decisions to non-technical executives, and working independently without a delivery management layer above you.
• Experience working in sprint-based, high-velocity delivery cycles where scope is defined, engineered, and shipped within weeks rather than quarters.

PREFERRED QUALIFICATIONS

Prior experience in a technology consulting, systems integrator, or AI services firm with pharma or life sciences clients — specifically in delivery roles rather than advisory.

• Experience leading engineering teams of three or more people across concurrent AI workstreams, with accountability for technical quality and delivery timelines
• Familiarity with AI governance frameworks, responsible AI design, and pharma-specific regulatory AI considerations including FDA AI/ML Software as a Medical Device guidance, ICH guidelines applicable to AI-generated evidence, and GxP data integrity requirements.
• Industry certification on one or more of the following is a big plus: AWS Certified AI Practitioner (AIF-C01), Databricks Certified Generative AI Engineer Associate, SnowPro Advanced Data Engineer (DEA-C01), or Claude Certified Architect Foundations (CCA-F)

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