United States
B&A
Insight Global
AI Engineer
United States
Dunwoody, Georgia, United States
Python
Overview
You will design and build agentic workflows — multi-step, multi-agent systems that reason, use tools, call external services, and recover from failure — and take them from prototype to reliable production. You turn business requirements into working agent systems: composing agents, designing the prompts and behaviors that drive them, wiring in the tools and data sources they need, choosing how the agents coordinate, and deciding where a human needs to stay in the loop. You build on top of our agent development tooling, so much of the surrounding machinery — identity, guardrails, logging, and monitoring — is handled for you; your focus is assembling those pieces into something that solves a real problem and behaves predictably even when the underlying models do not. The work is hands-on and product-facing: you translate requirements into designs, get them working against real data, harden them for production, and keep improving them once they are live.
Responsibilities
- Design and build agentic workflows — multi-step, multi-agent systems that reason, call tools, and recover from failure — owning them from design through production.
- Compose agents into reliable pipelines that integrate with external APIs, internal services, and data sources, with clear error handling and retry semantics.
- Design and iterate on the prompts and behaviors that drive each agent, treating them as versioned, tested artifacts rather than inline strings, and validating them against evaluation sets.
- Build the tools and integrations agents rely on — well-typed function interfaces or service endpoints — with attention to safety and correctness.
- Choose the right coordination approach for each task — a supervisor directing workers, a planner and executor, competing or critiquing agents, or a fixed sequence — and know when each fits.
- Decide where human review and approval belong, and build those checkpoints into longrunning workflows.
- Wire in state and memory appropriately — short-term working context and longer-term memory — so agents have what they need without leaking data across users or tenants.
- Build evaluation suites and regression checks that measure whether a workflow actually does its job — trajectory and outcome quality, not just pass/fail.
- Harden workflows for production — diagnosing timeouts, partial failures, unexpected model behavior, and state drift in long-running, non-deterministic systems.
- Instrument and debug agent behavior using tracing, logs, and cost and latency telemetry.
- Contribute to shared patterns, conventions, and reusable components that raise the quality bar for how agentic systems are built across the team.
- Partner with product and the broader engineering team to translate business requirements into agent designs that are practical to build, operate, and govern.
Qualifications
Required qualifications
- Strong software engineering fundamentals with production-grade Python — clean, tested code; comfortable with async patterns and long-running, stateful processes.
- Experience building or working with agentic or LLM-driven systems in a production or nearproduction setting — where a model drives control flow through tool use, multi-step reasoning, or agent-to-agent handoffs.
- Has composed multi-step or multi-agent workflows — orchestrating agents, managing shared state, chaining tool calls, and handling failures and retries.
- Practical prompt-engineering skill — can design prompts and system messages that make model behavior consistent, and has improved them against evaluations rather than by feel.
- Experience integrating with external APIs and services — REST, authentication, error handling, and designing for partial failure.
- Has built tools or integrations that a model or agent calls — function interfaces, schema design, or service endpoints.
- Disciplined about evaluation — can describe an eval suite they built for an agentic or LLM system, how they created the ground truth, and what it proved.
- Solid debugging instincts for distributed and non-deterministic systems — can reason through failures that span multiple agents or services.
- Writes meaningful tests for complex, stateful systems — unit, integration, and scenario-level coverage.
- Familiarity with agent or LLM observability — tracing, logging, or monitoring of model and tool calls.
- Background in backend engineering — APIs, queues, databases — that grounds you when workflows touch infrastructure.
- Exposure to multi-tenant systems and data-isolation concerns.
Preferred qualifications
- Experience with durable or long-running workflow execution — systems where state must survive restarts, retries, or partial failures.
- Familiarity with agent memory — working memory, long-term memory, and managing context windows.
- Has worked with retrieval-augmented generation and reranking — wiring retrieval into an agent workflow and measuring its effect.
- Exposure to human-in-the-loop patterns — approval steps, escalation policies, and review workflows in production.
- Experience with agent orchestration frameworks or patterns (LangGraph or comparable).
- Familiarity with prompt-injection and adversarial risks in agent and retrieval systems, and how to mitigate them.
- Comfortable working against an SDK or framework abstraction — understands its constraints and when to escalate gaps.
- Open-source contributions to agent frameworks or related tooling.
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