Forward Deployment Engineer (Pharma AI)

Location: Boston, MA, United States

Company

hirify.global is building a large Forward Deployment Engineering workforce focused on production AI deployments across pharmaceutical and life sciences organizations.

What you will do

• Embed in pharma client organizations and own the end-to-end design, build, and deployment of production AI systems on client infrastructure.
• Lead technical workstreams and direct Agent Pipeline Engineers and AI-Augmented Engineers across architecture and delivery.
• Manage technical relationships with Director, VP, and CDO-level stakeholders, including scoping, architecture trade-offs, and technical presentations.
• Architect and build multi-agent systems using orchestration, tool integration, RAG, memory, human-in-the-loop workflows, evaluation pipelines, and production MLOps.
• Develop AI evaluation and governance frameworks for regulated pharmaceutical environments, including quality thresholds, RAGAS, LLM-as-judge, red-teaming, and audit trails.
• Contribute reusable pharma AI architectures, deployment patterns, technical standards, mentoring, and delivery methodology.

Requirements

• 10+ years of experience in engineering, data engineering, analytics engineering, or AI/ML, including substantial direct pharma or life sciences client exposure.
• Mandatory pharma domain experience in commercial areas such as SFE, incentive compensation, market access, omnichannel, KAM, or patient services, or clinical areas such as trial operations, CDASH/SDTM, site analytics, RWE, HEOR, or regulatory data.
• Production AI engineering experience is required, including shipped LLM agents, RAG pipelines, multi-agent workflows, or clinical AI systems used in regulated environments.
• Strong Python, SQL, cloud infrastructure, data pipeline architecture, and API design skills, with production-grade engineering practices.
• Depth in at least one of Databricks, Snowflake, AWS Bedrock, or the Claude and Anthropic API stack, with working fluency across the others.
• Experience presenting architecture decisions to senior stakeholders, working independently, and delivering in sprint-based cycles measured in weeks.

Nice to have

• Technology consulting, systems integration, or AI services experience with pharma or life sciences clients.
• Experience leading teams of three or more engineers across concurrent AI workstreams.
• Knowledge of responsible AI, FDA AI/ML guidance, ICH guidelines, GxP data integrity, and pharma AI governance.
• Industry certification in AWS AI, Databricks Generative AI, Snowflake Data Engineering, or Claude architecture.

Culture & Benefits

• Direct client-embedded delivery rather than external advisory work.
• Work on production AI systems used in commercial and clinical pharma operations.
• Opportunities to mentor engineers and contribute to reusable technical standards and deployment methodology.