Forward Deployment Engineer — New Jersey, United States

About the Role

An Forward Deployment Engineer (FDE) sits at the intersection of AI platform capabilities and business users — translating ambiguous business problems into working AI solutions, deploying them into the customer's environment, and iterating in tight loops until they deliver measurable value.

You will be the technical person closest to the client: sitting with platform owners and Enterprise Architects, understanding how their systems really work, and building agentic and GenAI proofs of concept that run on those systems. You are part engineer, part consultant, and part translator between business, architecture and delivery.

What You'll Do

• Deliver end-to-end AI solutions — Take a specific business problem from first conversation with the stakeholder through deployment, adoption and a measurable result, learning the existing systems, data flows, integrations and constraints along the way.

• Embed with the client — Work day-to-day with client’s platform owners and Enterprise Architects to map current-state platforms, data flows, integration points and constraints.

• Build in governance from day one — Work with the GRC/compliance lead to apply data privacy, auditability, model risk and GxP-aware controls in everything you build.

• Translate ambiguous, evolving business requirements into working software — Turn fuzzy requirements into working software. Often there is no formal spec, so you work side by side with the people who own the problem and shape the solution with them.

• Build vertical AI applications rapidly — on top of the existing AI platform, using Snowflake Cortex, Databricks Genie, RAG pipelines and agentic workflows to solve narrow problems that carry high business value.

• Prototype in days, not months — Get a working Streamlit app, Databricks App or light web UI to business users within the first 1-2 weeks, then refine it from their real feedback.

• Deploy solutions inside the customer's regulated environment — respecting existing governance, RBAC, data residency, audit, and compliance constraints.

• Instrument for measurement — every deployed solution ships with usage metrics, adoption tracking, and business outcome telemetry from day one.

• Own the "last mile" that makes AI actually usable — data quirks, business rule exceptions, edge cases, user training, change management, and adoption support.

• Work directly with business functions — translating their language into technical solutions and back.

• Build reusable patterns — after solving a specific problem, extract the reusable pieces (prompts, retrieval patterns, UI components, evaluation harnesses) into shared assets other FDEs can leverage.

• Accountable for the outcome, not just the code – if the business user isn't getting value, the job isn't done regardless of the code being deployed.

Must-Have Technical Skills

• Agentic and GenAI — LangChain, LangGraph, LlamaIndex, Semantic Kernel, Model Context Protocol (MCP), multi-agent patterns – with production usage, not tutorials

• Evaluation and safety — LLM evaluation frameworks, guardrails, red-teaming, prompt-injection defences

• Full-stack AI application development — End-to-end working system, with a UI, a backend and an LLM integration, in weeks rather than months

• Python + FastAPI/Flask for backend services, Streamlit / Databricks Apps / lightweight React for user-facing interfaces

• Direct hands-on with at least one of: Snowflake Cortex (Analyst/Search/Agents/LLM Functions) OR Databricks Genie (Genie Spaces, semantic models) — you know these products well enough to configure, tune, and integrate them into vertical solutions

• RAG pipeline construction — chunking, embeddings, vector search (Pinecone, pgvector, Chroma, FAISS, Azure AI Search, Snowflake Cortex Search), retrieval evaluation, grounding, citation

• Prompt engineering with evaluation discipline — you know how to design prompts, evaluate them against ground truth, iterate based on hallucination and accuracy metrics

• Cloud data platform fluency — Snowflake and/or Databricks at working depth, plus at least one cloud provider (Azure preferred given the client environment, AWS/GCP acceptable) and it’s AIML services.

• SQL and data modelling — sufficient to work directly with governed datasets and semantic models

• Git, CI/CD, and modern development workflows — you own the deployment path, not just the local development

• API design and integration — you can integrate your solution into existing enterprise systems (SAP, Workday, Coupa, ERP, etc.) via REST APIs

• Basic MLOps awareness — you understand model versioning, prompt versioning, evaluation harnesses, observability (LangSmith, Datadog, Application Insights) — enough to hand off your solution to the MLOps team cleanly

Must-Have Non-Technical Skills

• Direct customer/stakeholder communication — you can sit in a room with a CFO office user, understand what's frustrating them, and translate that into a technical roadmap without needing a business analyst intermediary

• Ambiguity tolerance — you're comfortable starting work with a vague problem statement and refining it through prototypes rather than requiring detailed specs upfront

• Product-shaped thinking — you optimize for user adoption and business outcome, not for elegant architecture or full feature completeness

• Speed-to-first-demo mindset — you'd rather ship a rough working prototype in Week 1 than a polished spec in Week 4

• Willingness to write throwaway code — you know when to build for permanence and when to build for a demo; you don't over-engineer

• Change management sensibility — you understand that adoption requires more than good technology, and you're willing to do the user-training and hand-holding work to make solutions stick

Nice to Have

• Pharma, life sciences, or CPG Finance domain experience — familiarity with FP&A processes, financial consolidation, regulatory reporting, cost allocation, or clinical trial finance

• Veeva CRM, IQVIA, SAP S/4HANA, SAP BW, Oracle Financials, Workday Adaptive or similar enterprise Finance tooling

• Regulated environment delivery — SOX, GxP, data residency, audit trails

• Snowflake Cortex certification, Databricks certification, or Azure AI Engineer Associate (AI-102)

• Prior FDE, Solutions Engineer, Sales Engineer, or Field Engineer experience at Palantir, Snowflake, Databricks, OpenAI, Anthropic, or similar

• Startup / small-team experience — you've had to wear multiple hats and ship end-to-end without organizational scaffolding

• Direct experience with agentic workflows (multi-agent orchestration, human-in-the-loop, tool-calling) in production, not just demos

R2tYAX FMefqx — Finance Focus

• Working understanding of Finance business processes — order-to-cash, procure-to-pay, record-to-report, plan-to-report, close cycles, financial planning and analysis, management reporting, statutory reporting, tax reporting

• Familiarity with common Finance data — general ledger, cost centers, profit centers, chart of accounts, hierarchies, allocations, KPIs (revenue, gross margin, EBITDA, OPEX, working capital, DSO, DPO)

• Ability to speak Finance's language — variance analysis, forecasts vs actuals, budget vs actual, trend analysis, drill-through, drill-down, scenario planning

• Comfort with governed enterprise data — understanding why Finance data has to be trusted, auditable, and lineage-tracked, and why "just run an LLM on the raw data" is not an acceptable answer in a regulated environment

What We're NOT Looking For

• Data Scientists — this is not model training, fine-tuning, or ML research. The models are already there. This role is about applying them.

• Pure Backend Engineers — this role needs someone comfortable owning the full stack including the user-facing surface, not just APIs

• Pure ML Engineers / MLOps Engineers — those are separate roles. FDEs deploy AI value; MLOps operates AI infrastructure; DE engineers build the AI-ready data. Adjacent, not the same.

• Architects who don't ship code — this is a builder role. If your resume is 5 years of "architected" without "built," this is not your role.

• Consulting-shaped engineers who need a full spec — you'll be starting from ambiguous problem statements and iterating; if that scares you, this isn't the role.

• AI Platform Engineers who don't want customer contact — the customer conversation is a core part of the job, not a burden to be minimized.