Forward Deployed Engineer

Forward Deployed Engineer (AI / Data / Systems) Overview We’re hiring Forward Deployed Engineers to build and deploy AI systems in real-world environments. This is a client-embedded engineering role. You will take ambiguous problems, design technical solutions, and ship production systems that integrate LLMs, data infrastructure, and operational workflows. You are not building demos—you are building systems that are used, measured, and iterated on. What You’ll Do Build Production AI Systems Design and deploy LLM-powered applications using APIs (OpenAI, Anthropic, open-source models) Implement retrieval-augmented generation (RAG) pipelines using vector databases (Pinecone, Weaviate, FAISS) Build agent-based systems using orchestration frameworks (LangChain, LangGraph, custom agents) Develop evaluation pipelines for non-deterministic outputs (prompt testing, scoring, guardrails) Data & Systems Engineering Build and optimize data pipelines (batch + streaming) using tools like Airflow, Kafka, or similar Design data models and integrate structured/unstructured data sources (APIs, warehouses, document stores) Work with data platforms such as Snowflake, Databricks, BigQuery, or Postgres Implement caching, indexing, and retrieval strategies for performance and cost optimization Backend & Infrastructure Build scalable backend services and APIs (Python, FastAPI, Node.js, TypeScript) Deploy services using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure) Implement CI/CD pipelines and versioning for models, prompts, and workflows Manage secrets, auth, and secure system integrations in production environments Agentic Workflows & Automation Design multi-step agent workflows with tool use, memory, and state management Implement function calling, tool routing, and structured output handling Build systems that combine LLM reasoning with deterministic business logic Client-Embedded Execution Work directly with stakeholders to translate business problems into technical systems Iterate rapidly in live environments with real users and feedback loops Own delivery from prototype → production → optimization What You Bring Engineering Depth 3–8+ years of software engineering experience Strong proficiency in Python and/or TypeScript Experience building distributed systems, APIs, and data-intensive applications AI / LLM Experience Hands-on experience with: Prompt engineering and evaluation RAG architectures and vector search LLM orchestration frameworks (LangChain, LangGraph, or similar) Understanding of model limitations (hallucinations, latency, cost tradeoffs, context windows) Data & Infrastructure Experience with databases (SQL + NoSQL), data pipelines, and ETL processes Familiarity with cloud infrastructure and containerized deployments Understanding of system performance, observability, and scaling Builder Mindset You operate well in ambiguity and move quickly from idea → implementation You debug systems end-to-end (not just your layer) You care about whether the system actually works in production Communication Able to work directly with non-technical stakeholders Can explain system design, tradeoffs, and limitations clearly Comfortable operating in client-facing environments What Sets You Apart Experience deploying AI systems in production environments at scale Built agent-based systems or internal AI tooling used by real users Experience with: Embedding models and semantic search Evaluation frameworks (human-in-the-loop, automated scoring) Prompt/version management systems Background in startups, consulting, or high-ownership environments Why This Role You’ll build systems that are actually used—not prototypes You’ll work across the full stack of modern AI systems You’ll have ownership over real outcomes, not just tickets You’ll operate at the edge of applied AI in production environments Show more Show less