I
Jr AI Engineer (Chi)
Insight Global
2 hours ago
Full-time
On-site
Lincolnshire, Illinois, United States
Job Description
A large retailer is building the internal platform that enables Good Sam departments to talk with
purpose-trained AI agents to answer real questions. We are seeking an AI Engineer who can design and
build agent architecture, implement LLM pipelines, validate their outputs rigorously, and work directly with
non-technical stakeholders to scope and ship new agents. This role blends Data Engineering, Data
Architecture, Backend Software Engineering, and AI Orchestration. You will work inside a small,
tightly-scoped squad whose mission is shipping production-quality agents on a defined staged build plan.
What You'll Actually Be Building
Agent Platform & Orchestration
• Build and extend our agent-serving architecture: a hierarchical orchestrator routing questions through
specialized agents, with all LLM calls governed through a gateway for per-agent cost attribution, rate
limiting, and circuit breaking.
• Bridge internal-only systems (Snowflake, internal Postgres, internal APIs) to agents securely via MCP or
custom tool patterns, without ever exposing internal infrastructure to the public internet.
• Design and wire multi-agent workflows using frameworks like LangGraph, LangChain, or agentic
harnesses (Deep Agents) where the task genuinely requires fan-out, specialized sub-agents, or a
dedicated verifier step — and just as importantly, know when a single well-built agent loop is the right
call instead.
• Implement Retrieval-Augmented Generation (RAG) for department knowledge bases: hierarchical
chunking, hybrid search, and selective retrieval so agents ground answers in real company content
instead of hallucinating.
AI-Accelerated Delivery
• Use Claude Code and similar AI coding tools as the primary means of writing, refactoring, and testing
code across the stack – direct, prompt, review, and harden AI-generated output rather than hand-writing
every line.
• Translate department requirements into precise structured specs and agent instructions (system
prompts, tool schemas, knowledge files) that produce reliable, production-ready behavior.
• Build and maintain a golden question set for each agent domain – question-to-expected-result pairs that
run in CI and catch regressions before they reach stakeholders.
• Maintain sound judgment on when to trust AI-generated code versus when to intervene manually
speed never substitutes for correctness on a shipped agent.
Governance & Reliability
• Build in security and cost controls including per-agent spend caps and rate limits, human-approval gates
on any consequential action, read-only enforcement, etc.
• Implement audit logging for every tool call, session, and human approval — tie every action back to the
requesting user's identity.
• Apply a progressive-autonomy rollout: pilot with one team before wider release, gate write/action
capability behind a proven read/advise phase. Autonomous write-backs are explicitly out of scope at
launch – agents answer questions, they do not initiate actions.
• Versioned, human-reviewed knowledge artifacts (retrieval documents, prompt playbooks) for teaching an
agent department-specific knowledge.
Cross-Functional Collaboration
• Partner directly with department stakeholders (Sales, Marketing, etc.) to scope what their agent actually
needs to answer, gather real stakeholder phrasing for the golden question set, and communicate
technical tradeoffs in plain terms.
• Document architecture decisions and open questions as you go – this team keeps a living internal
knowledge base of what's been tried, what worked, and what's still unresolved. You are expected to
contribute to it.
• Mentor other engineers on agent-building patterns, eval design, and validation discipline as the team and
platform grow.
Pay rate: 45-55/hour
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
2+ years of software engineering experience with strong backend fundamentals in Python – sufficient to
read, debug, and extend AI-generated code with confidence, not just run it.
• Hands-on experience building with LLMs: prompt engineering, tool/function calling, structured output
parsing, and evaluating model output with engineering rigor – you can explain why an agent failed and
what specifically you would change.
• Experience with at least one agent orchestration pattern or framework (LangGraph, LangChain, a
manual tool-use loop, or a comparable approach acceptable) – you understand what a graph edge is
and why it matters for retry and routing logic.
• Solid SQL and data warehouse proficiency – Snowflake experience strongly preferred. You can write
and debug CTEs, window functions, and joins, and you understand why a query returns zero rows
without panicking.
• Direct experience directing AI coding assistants (Claude Code, Cursor, GitHub Copilot, or similar) to
generate and modify production code, with demonstrated spec-writing skill.
• Solid understanding of API design, integration patterns, and data modeling – you design integrations, not
just consume them.
• Familiarity with LLM eval concepts: golden test sets, LLM-as-judge, precision/recall for RAG, eval-driven
development. You've built or contributed to an eval harness, not just run one.
• Experience with Git and standard CI/CD practices.
• Comfort operating in a fast, iteration-heavy delivery model with sound judgment about where rigor
(security, SQL safety, human approval gates, eval discipline) cannot be compressed Experience with Model Context Protocol (MCP) – server/tool integration or permission policies.
• Familiarity with RAG architectures: chunking strategies, hybrid dense/sparse search, pgvector or
comparable vector stores, cross-encoder reranking. Qdrant or RAGFlow experience is a plus but not
required – our primary vector store is pgvector in Postgres.
• Experience with MLflow for LLM observability: experiment tracking, trace inspection, LangGraph
autologging.
• Experience with LLM API gateways or proxy layers (Bifrost, LiteLLM, PortKey, or similar)
understanding of virtual keys, spend caps, and per-agent routing.
• Familiarity with Airflow or comparable batch schedulers for running eval DAGs, schema refresh jobs, and
pre-computation pipelines.
• Understanding of AI governance concepts: sandboxing, network egress control, spend/budget caps,
audit logging, or human-in-the-loop approval design.
• Experience translating a business unit's ambiguous ask into a scoped, shipped technical solution.
• Experience with cloud platforms (Azure, AWS, or GCP) and containerization (Docker/Podman) – general
cloud breadth is a plus, though this role is not cloud-infrastructure-first.
A large retailer is building the internal platform that enables Good Sam departments to talk with
purpose-trained AI agents to answer real questions. We are seeking an AI Engineer who can design and
build agent architecture, implement LLM pipelines, validate their outputs rigorously, and work directly with
non-technical stakeholders to scope and ship new agents. This role blends Data Engineering, Data
Architecture, Backend Software Engineering, and AI Orchestration. You will work inside a small,
tightly-scoped squad whose mission is shipping production-quality agents on a defined staged build plan.
What You'll Actually Be Building
Agent Platform & Orchestration
• Build and extend our agent-serving architecture: a hierarchical orchestrator routing questions through
specialized agents, with all LLM calls governed through a gateway for per-agent cost attribution, rate
limiting, and circuit breaking.
• Bridge internal-only systems (Snowflake, internal Postgres, internal APIs) to agents securely via MCP or
custom tool patterns, without ever exposing internal infrastructure to the public internet.
• Design and wire multi-agent workflows using frameworks like LangGraph, LangChain, or agentic
harnesses (Deep Agents) where the task genuinely requires fan-out, specialized sub-agents, or a
dedicated verifier step — and just as importantly, know when a single well-built agent loop is the right
call instead.
• Implement Retrieval-Augmented Generation (RAG) for department knowledge bases: hierarchical
chunking, hybrid search, and selective retrieval so agents ground answers in real company content
instead of hallucinating.
AI-Accelerated Delivery
• Use Claude Code and similar AI coding tools as the primary means of writing, refactoring, and testing
code across the stack – direct, prompt, review, and harden AI-generated output rather than hand-writing
every line.
• Translate department requirements into precise structured specs and agent instructions (system
prompts, tool schemas, knowledge files) that produce reliable, production-ready behavior.
• Build and maintain a golden question set for each agent domain – question-to-expected-result pairs that
run in CI and catch regressions before they reach stakeholders.
• Maintain sound judgment on when to trust AI-generated code versus when to intervene manually
speed never substitutes for correctness on a shipped agent.
Governance & Reliability
• Build in security and cost controls including per-agent spend caps and rate limits, human-approval gates
on any consequential action, read-only enforcement, etc.
• Implement audit logging for every tool call, session, and human approval — tie every action back to the
requesting user's identity.
• Apply a progressive-autonomy rollout: pilot with one team before wider release, gate write/action
capability behind a proven read/advise phase. Autonomous write-backs are explicitly out of scope at
launch – agents answer questions, they do not initiate actions.
• Versioned, human-reviewed knowledge artifacts (retrieval documents, prompt playbooks) for teaching an
agent department-specific knowledge.
Cross-Functional Collaboration
• Partner directly with department stakeholders (Sales, Marketing, etc.) to scope what their agent actually
needs to answer, gather real stakeholder phrasing for the golden question set, and communicate
technical tradeoffs in plain terms.
• Document architecture decisions and open questions as you go – this team keeps a living internal
knowledge base of what's been tried, what worked, and what's still unresolved. You are expected to
contribute to it.
• Mentor other engineers on agent-building patterns, eval design, and validation discipline as the team and
platform grow.
Pay rate: 45-55/hour
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
2+ years of software engineering experience with strong backend fundamentals in Python – sufficient to
read, debug, and extend AI-generated code with confidence, not just run it.
• Hands-on experience building with LLMs: prompt engineering, tool/function calling, structured output
parsing, and evaluating model output with engineering rigor – you can explain why an agent failed and
what specifically you would change.
• Experience with at least one agent orchestration pattern or framework (LangGraph, LangChain, a
manual tool-use loop, or a comparable approach acceptable) – you understand what a graph edge is
and why it matters for retry and routing logic.
• Solid SQL and data warehouse proficiency – Snowflake experience strongly preferred. You can write
and debug CTEs, window functions, and joins, and you understand why a query returns zero rows
without panicking.
• Direct experience directing AI coding assistants (Claude Code, Cursor, GitHub Copilot, or similar) to
generate and modify production code, with demonstrated spec-writing skill.
• Solid understanding of API design, integration patterns, and data modeling – you design integrations, not
just consume them.
• Familiarity with LLM eval concepts: golden test sets, LLM-as-judge, precision/recall for RAG, eval-driven
development. You've built or contributed to an eval harness, not just run one.
• Experience with Git and standard CI/CD practices.
• Comfort operating in a fast, iteration-heavy delivery model with sound judgment about where rigor
(security, SQL safety, human approval gates, eval discipline) cannot be compressed Experience with Model Context Protocol (MCP) – server/tool integration or permission policies.
• Familiarity with RAG architectures: chunking strategies, hybrid dense/sparse search, pgvector or
comparable vector stores, cross-encoder reranking. Qdrant or RAGFlow experience is a plus but not
required – our primary vector store is pgvector in Postgres.
• Experience with MLflow for LLM observability: experiment tracking, trace inspection, LangGraph
autologging.
• Experience with LLM API gateways or proxy layers (Bifrost, LiteLLM, PortKey, or similar)
understanding of virtual keys, spend caps, and per-agent routing.
• Familiarity with Airflow or comparable batch schedulers for running eval DAGs, schema refresh jobs, and
pre-computation pipelines.
• Understanding of AI governance concepts: sandboxing, network egress control, spend/budget caps,
audit logging, or human-in-the-loop approval design.
• Experience translating a business unit's ambiguous ask into a scoped, shipped technical solution.
• Experience with cloud platforms (Azure, AWS, or GCP) and containerization (Docker/Podman) – general
cloud breadth is a plus, though this role is not cloud-infrastructure-first.