AI Agentic Engineer

Profitmind is a retail analytics SaaS company that turns competitive and customer data into agent-driven insights, helping retailers make faster, sharper merchandising and pricing decisions. Based in Pittsburgh and backed by a recent strategic investment from Accenture, we're scaling our agentic AI platform for some of the world's largest retailers.

Are you ready to apply Make sure you understand all the responsibilities and tasks associated with this role before proceeding.

Our platform runs a team of nine specialized agents — Data Load, Strategy, Competitive Intelligence, Pricing, Inventory, Promotions, Assortment, Planning, and the Monday Morning agent — that analyze a retailer's entire business every week and hand merchandising teams a ranked list of actions with the dollar value attached. This role builds the agentic stack that makes that possible.

The Role

We're hiring a multi-disciplinary AI Agentic Engineer to design, implement, and deploy LLM-driven agents with strong backend and front-end integration — across agent harness engineering, agent development, agentic workflows and platform integration, LLM inference/evaluation/hosting, and fine-tuning — building production-grade applications on top of all of it.

The ideal candidate combines a strong Python and AI foundation with hands-on LLM knowledge (prompt engineering, context management, structured outputs and tool calling, retrieval, evals, and fine-tuning with LoRA/QLoRA), harnesses (Claude Agent SDK, OpenAI Codex SDK, Pi, OpenClaw, Hermes Agent), and agent frameworks (LangGraph, PydanticAI, Google ADK).

What You'll Do

Agent Harness Engineering

• Build and own the harness layer — agent loops, tool execution, context management, session persistence, permission controls, and sandboxed environments — evaluating and extending existing harnesses where they beat building internally.
• Architect orchestration for multi-step planning, long-term memory, and dynamic tool use, with guardrails, fallbacks, and self-correction for timeouts, context overflows, and hallucinated tool calls.
• Standardize internal API contracts for tool creation so new enterprise data sources plug into the agent environment securely.

Agent Development

• Architect and build production-grade LLM agents (LangGraph, PydanticAI, Google ADK, or custom loops when a framework adds unnecessary complexity), with composable patterns for retrieval, planning, reflection, and subagent delegation.
• Integrate vector databases and knowledge graphs for RAG, memory, and grounded decision-making.
• Engineer context deliberately (just-in-time retrieval, progressive disclosure, compaction, structured note-taking, isolation) and evaluate prompt/tool strategies for reliability.

Agentic Workflows and Platform Integration

• Build multi-step, multi-agent workflows with routing, parallel execution, checkpointing, retries, compensation, and human approval steps that survive restarts and degrade gracefully.
• Build MCP servers for structured, auditable, least-privilege access to internal systems, and scalable FastAPI services for synchronous, asynchronous, and streaming execution.
• Connect agents to internal applications, chat platforms, triggers, and CI pipelines; build agent interfaces (React, TypeScript, Next.js) with real-time streaming (SSE/WebSockets) and clear UX for long-running agents — progress, interruption, steering, approval, recovery.

LLM Inference, Evaluation, and Hosting

• Evaluate and integrate managed or self-hosted models; monitor and improve quality, latency, reliability, and cost (caching, batching, model routing).
• Build task-specific evals and regression tests using Profitmind's real retail workflows, and instrument agent behavior — tool calls, errors, latency, tokens, cost.

LLM Fine-Tuning and Continuous Improvement

• Use eval results to decide between prompting, context engineering, retrieval, or fine-tuning; run targeted LoRA/QLoRA experiments when justified.
• Help curate and version evaluation and training data from representative business use cases.

What We're Looking For

• Education and foundation. Bachelor's or master's in computer science or related, or equivalent practical experience, with 2+ years building agentic applications.
• Production agent experience. 1+ year operating LLM agents in production, with frameworks (LangGraph, PydanticAI) and SDKs/harnesses (Claude Agent SDK, OpenAI Codex SDK).
• Agent architecture. Deep understanding of agent loops, tool calling, retrieval, and context management, with proven ability to measure and improve agent performance.
• Evaluation and fine-tuning. Experience evaluating non-deterministic AI systems and fine-tuning or adapting models for domain-specific tasks.
• Backend and data infrastructure. Production experience with FastAPI, Docker, MLOps practices, and vector stores.
• AI-assisted engineering. Proficiency with agentic coding assistants (Claude Code, Codex, Cursor), folding new tooling into how you work rather than around it.

Nice to Have

• Hands-on fine-tuning (LoRA or QLoRA) and curating evaluation or training datasets
• Knowledge graphs alongside vector retrieval for grounded decision-making
• Serving self-hosted or open-weight models, including caching, batching, and model routing
• Front-end depth in React, TypeScript, or Next.js, including real-time streaming
• Retail or commerce domain experience

Location

Hybrid - Pittsburgh, PA, full-time, on the Machine Learning team.

Why This Role

You'll shape a production AI platform used to make high-value retail decisions, with ownership across the full agent lifecycle — from experimentation and evaluation through deployment and user experience. We offer a competitive salary and equity, and a flexible hybrid working environment.

Profitmind is an equal opportunity employer. xhyhwjd We welcome applicants of all backgrounds.