Forward Deployed Engineer

Title: Forward Deployed Engineer

• The FDE turns AI products into tools Client's commercial users actually adopt, measured by business outcomes rather than delivered features. They sit with brand, field and analytics teams, understand the real workflow, then configure, integrate and extend the product until it works on Client's data and systems.
• The role blends three jobs: software engineer who writes production code, solutions architect who fits AI into the enterprise stack, and consultant who scopes problems to value.
• The FDE owns a use case end to end, from discovery through pilot, production and scale.
• They are also the bridge back to the core product team. Patterns seen across Client's brands become reusable components, accelerators and product requirements.

Commercial AI use cases in scope

The FDE will deliver across seven commercial domains; priorities will be set with Clienr's brand and commercial leadership.

Key responsibilities

The FDE owns five stages of each use case, from discovery to feeding learnings back into the product.

1. Discover and scope

• Shadow brand, field and analytics users to map workflows, pain points and the decision the AI must improve.
• Turn business problems into scoped use cases with a value hypothesis, success metrics and data requirements.
• Run fast feasibility checks on data availability, quality and access rights before committing to a build.

2. Build and integrate

• Configure, extend and integrate the AI product with Pfizer's CRM, data platform, content systems and identity services.
• Build data pipelines, retrieval (RAG) pipelines, prompts, agents and evaluation harnesses in production-grade code.
• Prototype in days on approved data, then harden to production in weeks.

3. Deploy and operate

• Take solutions through Pfizer Digital's architecture, security, privacy and responsible-AI reviews.
• Set up CI/CD, monitoring, LLM evaluation, guardrails and cost and latency controls.
• Own post-launch reliability, model quality and drift until handover to run teams.

4. Drive adoption and value

• Run pilots with a defined brand, region or user group; train users and iterate on feedback.
• Measure and report adoption, time saved and commercial uplift against a baseline or control.
• Build the business case and rollout plan to scale from pilot to additional brands and markets.

5. Feed back to product

• Convert field learnings into product requirements and prioritized backlog items.
• Contribute reusable components, connectors and accelerators to the core platform.
• Represent the Pfizer commercial user in product and engineering decisions.

Required qualifications

Candidates need engineering depth, hands-on LLM delivery and real pharma commercial data experience; strength in only one is not enough.

Engineering

• 7+ years in software engineering, including 3+ years shipping data, ML or AI applications to production.
• Strong Python and SQL; working knowledge of TypeScript/React or similar for user-facing prototypes.
• Experience with APIs, microservices, containers and CI/CD on AWS, Azure or Google Cloud Platform.

AI and LLM applications

• 2+ years building LLM applications: RAG, tool-using agents, prompt design, structured outputs.
• Hands-on with LLM evaluation, guardrails, observability and cost and latency optimization.
• Practical grasp of classical ML for segmentation, propensity and forecasting models.

Pharma commercial data and process

• 3+ years working with life sciences commercial data: HCP/HCO master data, prescription and claims data, CRM call activity.
• Familiar with sources such as IQVIA, Symphony Health or Komodo, and CRMs such as Veeva or Salesforce Life Sciences Cloud.
• Understands territory alignment, targeting, incentive compensation, launch analytics and the brand planning cycle.

Client-facing and compliance

• Comfortable leading workshops and presenting to brand directors and VP-level commercial leaders.
• Working knowledge of MLR/PRC review, the PhRMA Code, HIPAA and adverse event reporting obligations.
• Bachelor's degree in computer science, engineering, data science or a related field.

Preferred qualifications

The strongest candidates have already done this job at an AI or data platform company, or inside a top-20 pharma commercial organization.

• Prior forward deployed, solutions or customer engineering role at an AI, data or analytics platform company.
• Delivered next-best-action, omnichannel orchestration or AI-assisted MLR content review in production.
• Experience with agent frameworks and protocols such as LangGraph, Claude Agent SDK, OpenAI Agents SDK or MCP.
• Experience with content platforms such as Veeva Vault PromoMats, Aprimo or Adobe Experience Manager.
• Experimentation and causal inference skills for measuring commercial uplift (test vs. control, holdouts).
• Background in a pharma commercial analytics consultancy or a top-20 pharma commercial function.
• Cloud or data platform certifications (AWS, Azure, Snowflake, Databricks); master's degree a plus.

Technology and data landscape

The FDE works across the full stack, from foundation models to the CRM screen a rep uses. Tools below are representative; the exact Pfizer stack is to be confirmed with Pfizer Digital.

Compliance and guardrails

The FDE designs compliance into every solution from day one, because commercial AI that fails review never reaches users.

• Promotional content: AI-drafted promotional material goes through MLR/PRC review, with every claim traceable to an approved reference and fair balance and ISI preserved.
• On-label only: guardrails block off-label claims and comparative statements not supported by approved labeling.
• Commercial–medical firewall: commercial tools do not give medical advice; medical questions route to Medical Information.
• Adverse events and product complaints: AI that touches call notes, chats or inquiries detects and routes AEs and PQCs per pharmacovigilance SOPs and timelines.
• Data privacy: HIPAA, data use agreements for de-identified data, and state privacy laws govern HCP and patient data; no PHI in prompts without approval.
• Responsible AI: solutions pass Pfizer's AI governance review, with human-in-the-loop for consequential outputs and explainable targeting models.
• Validation: commercial systems are usually non-GxP, but a risk-based CSA approach applies where a system feeds PV or other regulated records.

Success measures

By day 90, the FDE should have one commercial AI use case live with real users and a measured baseline.

Ongoing KPIs

• Adoption: weekly active users and share of the target user group using the tool.
• Efficiency: time saved per user, such as rep pre-call planning time or MLR review cycle time.
• Commercial impact: HCP engagement, next-best-action acceptance rate, and NBRx/TRx uplift vs. control.
• Quality: evaluation scores, error and hallucination rates, MLR first-pass approval rate.
• Velocity: time from approved use case to production; reusable components contributed to the platform.

Competencies and interview focus

Assess six competencies, using scenarios drawn from Pfizer commercial work rather than generic coding test