The Role
The Forward Deployed AI Engineer will work directly with clients and senior engineers to build AI-powered systems inside real enterprise environments. This role is ideal for a strong junior engineer who wants to work on practical AI systems, enterprise data, agents, retrieval, workflow automation, and context graphs. You will help connect to client systems, process messy real-world data, build prototypes, and turn business problems into working software.

The work is hands-on and client-facing:
• Understand operational workflows and convert them into technical artifacts.
• Connect to real enterprise systems and data sources.
• Build context-aware AI agents, retrieval systems, and automation prototypes.
• Help move the best prototypes toward reliable production deployments.

Requirements

Build context graphs. Help ingest, clean, structure, and connect data from enterprise systems into context graph. This may include structured databases, PDFs, spreadsheets, tickets, CRM data, analytics events, Slack or Teams exports, meeting transcripts, operational workflows, and other internal knowledge sources.

Develop AI and agentic workflows. Build AI pipelines and agent-based systems that can reason over enterprise context, identify patterns, surface workflow gaps, and suggest or trigger automations. This may involve LLMs, retrieval systems, structured extraction, tool use, LangGraph-style workflows, and agent harnesses.

Connect to enterprise systems. Integrate with client infrastructure such as databases, APIs, cloud storage, document repositories, analytics tools, ticketing systems, and internal applications.

Prototype automation opportunities. Rapidly build proof-of-concepts that show how AI can improve a client's operations - for example, process documentation, workflow discovery, incident management, document extraction, customer journey analysis, or operational decision support.

Turn messy business problems into software. Work with senior engineers and business stakeholders to translate ambiguous operational problems into technical designs, data models, prompts, pipelines, and deployed applications.

Support production deployments. Help build reliable, maintainable systems that can run in production environments, including client cloud environments when needed.

Ideal Candidate
We are looking for a strong junior software engineer who is excited about applied AI and wants to build real systems, not just experiments.

You do not need to be an expert in every area below, but you should be curious, technical, and comfortable learning quickly.

Technical Background
• Strong Python fundamentals.
• Basic backend development experience • Familiarity with APIs, databases, and cloud services.
• Interest in LLMs, agents, retrieval, structured outputs, and tool-calling.
• Familiarity with data pipelines and messy real-world datasets.
• Experience with FastAPI, LangChain, LangGraph, vector databases, document processing, or knowledge graphs is a plus.
• AWS experience is a plus.
• Experience with enterprise data, analytics, or workflow automation is a strong plus.

What Makes Someone Successful in This Role
• You like figuring out how businesses actually operate
• You are comfortable working with incomplete, messy, or poorly documented data
• You can move quickly from vague requirements to a working prototype
• You care about building useful systems, not just impressive demos
• You communicate clearly with both engineers and non-technical stakeholders
• You are curious about how AI agents can interact with real tools, data, and workflows
• You want to learn how to deploy AI into real enterprise environments

Why This Role Is Interesting
You will work on the frontier of practical enterprise AI: connecting AI agents to real company context and using that context to discover and automate high-value workflows.

This is a hands-on engineering role for someone who wants to grow into building production-grade AI systems across data, agents, infrastructure, and enterprise software