Forward Deployed Engineer

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.

• Design and deploy LLM-powered applications using APIs (OpenAI, Anthropic, open-source models)
• Build agent-based systems using orchestration frameworks (Lang Chain, Lang Graph, custom agents)
• Develop evaluation pipelines for non-deterministic outputs (prompt testing, scoring, guardrails)
• 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, Big Query, or Postgres
• Implement caching, indexing, and retrieval strategies for performance and cost optimization
• Build scalable backend services and APIs (Python, FastAPI, Node.js, Type Script)
• 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

• 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

• Work directly with stakeholders to translate business problems into technical systems
• Iterate rapidly in live environments with real users and feedback loops

• 3–8+ years of software engineering experience
• Strong proficiency in Python and/or Type Script
• Experience building distributed systems, APIs, and data-intensive applications

• Hands-on experience with:
• Prompt engineering and evaluation
• RAG architectures and vector search
• LLM orchestration frameworks (Lang Chain, Lang Graph, or similar)
• Understanding of model limitations (hallucinations, latency, cost tradeoffs, context windows)

• Experience with databases (SQL + No
  
  SQL), data pipelines, and ETL processes
• Familiarity with cloud infrastructure and containerized deployments
• Understanding of system performance, observability, and scaling

• You operate well in ambiguity and move quickly from idea → implementation
• You care about whether the system actually works in production

• Able to work directly with non-technical stakeholders
• Can explain system design, tradeoffs, and limitations clearly
• Comfortable operating in client-facing environments

• 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)
• Background in startups, consulting, or high-ownership environments

• 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