Forward Deployed Engineer

Forward Deployed Engineer (AI / Data / Systems)OverviewWe’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 DoBuild Production AI SystemsDesign 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 EngineeringBuild and optimize data pipelines (batch + streaming) using tools like Airflow, Kafka, or similarDesign data models and integrate structured/unstructured data sources (APIs, warehouses, document stores)Work with data platforms such as Snowflake, Databricks, BigQuery, or PostgresImplement caching, indexing, and retrieval strategies for performance and cost optimizationBackend & InfrastructureBuild 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 workflowsManage secrets, auth, and secure system integrations in production environmentsAgentic Workflows & AutomationDesign multi-step agent workflows with tool use, memory, and state managementImplement function calling, tool routing, and structured output handlingBuild systems that combine LLM reasoning with deterministic business logicClient-Embedded ExecutionWork directly with stakeholders to translate business problems into technical systemsIterate rapidly in live environments with real users and feedback loopsOwn delivery from prototype → production → optimizationWhat You BringEngineering Depth3–8+ years of software engineering experienceStrong proficiency in Python and/or TypeScriptExperience building distributed systems, APIs, and data-intensive applicationsAI / LLM ExperienceHands-on experience with:Prompt engineering and evaluationRAG architectures and vector searchLLM orchestration frameworks (LangChain, LangGraph, or similar)Understanding of model limitations (hallucinations, latency, cost tradeoffs, context windows)Data & InfrastructureExperience with databases (SQL + NoSQL), data pipelines, and ETL processesFamiliarity with cloud infrastructure and containerized deploymentsUnderstanding of system performance, observability, and scalingBuilder MindsetYou operate well in ambiguity and move quickly from idea → implementationYou debug systems end-to-end (not just your layer)You care about whether the system actually works in productionCommunicationAble to work directly with non-technical stakeholdersCan explain system design, tradeoffs, and limitations clearlyComfortable operating in client-facing environmentsWhat Sets You ApartExperience deploying AI systems in production environments at scaleBuilt agent-based systems or internal AI tooling used by real usersExperience with:Embedding models and semantic searchEvaluation frameworks (human-in-the-loop, automated scoring)Prompt/version management systemsBackground in startups, consulting, or high-ownership environmentsWhy This RoleYou’ll build systems that are actually used—not prototypesYou’ll work across the full stack of modern AI systemsYou’ll have ownership over real outcomes, not just ticketsYou’ll operate at the edge of applied AI in production environments