AI Engineer, Forward Deployed (Claude)

Collectiv•Chicago, IL

1d•Remote

About The Position

The AI Engineer is a hands-on builder who works directly with Client teams to design, build, and ship production AI solutions on Anthropic's Claude platform. This is an individual contributor role. You will write the code, build the agents and integrations, connect Claude to Client data, and get solutions into the hands of real users. You will work inside a Collectiv delivery team alongside a project lead, architects, and data engineers. You will partner directly with Client business and IT stakeholders to turn a business problem into a working Claude solution, then harden it for production on the Microsoft data stack and beyond.

Requirements

Bachelor's degree from an accredited university in Computer Science, Data Science, Engineering, or a related field (or equivalent experience)
4+ years of experience in software engineering, data engineering, or analytics engineering
1+ years of hands-on experience building and deploying generative AI or LLM-powered solutions in production or Client environments
Hands-on experience with the Claude API, including tool use
Experience building or deploying MCP servers
Hands-on experience with Claude Code or the Claude Agent SDK
Strong Python or TypeScript skills
Experience building RAG solutions, including vector search and document ingestion
Experience working in at least one major cloud (Azure preferred, AWS or Google Cloud also valued)
Ability to work independently in a Client-facing consulting role

Nice To Haves

Experience deploying Claude through Microsoft Foundry, Amazon Bedrock, or Google Vertex AI
Experience rolling out Claude for Enterprise or Claude for Work to business users
Experience with Microsoft Fabric, Databricks, or Power BI
Experience with LLMOps practices including evaluation frameworks, prompt management, and model monitoring
Familiarity with security, privacy, and compliance considerations for AI in regulated industries
Consulting experience across multiple Clients or industries
Anthropic training or certifications, or relevant Azure certifications

Responsibilities

Build agents, assistants, and knowledge search experiences using the Claude API, including tool use, extended thinking, structured outputs, prompt caching, and batch processing
Build agentic workflows with the Claude Agent SDK, including multi-step orchestration, subagents, and human-in-the-loop controls
Build and deploy Model Context Protocol (MCP) servers that connect Claude to Client systems such as Microsoft Fabric, Power BI, SharePoint, CRM, and ERP platforms
Deploy Claude through the Anthropic API, Microsoft Foundry, Amazon Bedrock, or Google Vertex AI based on Client requirements
Write and iterate on system prompts, tool definitions, and Agent Skills based on test results and user feedback
Configure and roll out Claude for Enterprise, including connectors, Projects, Skills, and admin controls
Use Claude Code to accelerate delivery, and help Client engineering teams adopt it with sound workflows, permissions, and guardrails
Build reusable Skills, plugins, and prompt libraries that encode Client processes and standards
Build end-to-end retrieval-augmented generation (RAG) pipelines including ingestion, chunking, embedding, vector and hybrid search, and grounding
Partner with Collectiv data engineers to connect Claude solutions to Microsoft Fabric, Databricks, and other enterprise data platforms
Integrate Claude solutions with enterprise systems through APIs, Azure Functions, and event-driven architectures
Build evaluation sets and test harnesses that measure answer quality, groundedness, hallucination rate, safety, latency, and cost
Implement logging, monitoring, tracing, and cost controls for deployed agents
Apply security, identity, and data governance controls appropriate to each Client environment
Use version control, CI/CD, and code review standards for all AI solution code and configuration
Work directly with Client stakeholders to gather requirements, run working sessions, demo progress, and manage expectations
Translate business problems into scoped, buildable AI use cases with clear success metrics
Train Client users and engineers on the solutions you build and support adoption after go-live
Contribute reusable patterns, accelerators, and documentation to Collectiv's AI practice