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Acuver Consulting Logo Acuver Consulting · IT - Software Development

Technical Lead – AI/ML

United States, Atlanta · Job Posted June 09, 2026
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Job Description

Technical Lead – AI/ML to lead the design and delivery of enterprise-grade agentic AI systems for clients in commerce, retail, fulfillment, and supply chain. Hands-on leadership role: set technical direction, architect and build production agentic pipelines, mentor a small team. Location – USA (Remote).

Job Responsibility

  • Own end-to-end technical design and delivery of enterprise-grade agentic AI systems — from architecture through production, reliability, and handover
  • Lead a small engineering pod: set technical direction, run design and code reviews, mentor engineers, and remain hands-on writing production code yourself
  • Translate ambiguous business problems in commerce, fulfillment, and supply chain into well-scoped agentic workflows with clear success metrics and guardrails
  • Architect multi-agent pipelines with tool/function calling, retrieval (RAG), memory, orchestration, evaluation, and human-in-the-loop controls
  • Establish engineering standards for agent evaluation, observability, safety, cost, and latency, and drive them across the pod
  • Partner directly with client stakeholders and SMEs — leading discovery, shaping solution architecture, and presenting trade-offs to technical and executive audiences
  • Build and deploy Model Context Protocol (MCP) servers and reusable tools/integrations that let agents act safely across enterprise systems (OMS, WMS, CRM, data and commerce platforms)
  • Make pragmatic build-vs-buy and framework decisions and collaborate cross-functionally on requirements, sprint planning, and delivery
  • Lead the optimization phase: design optimization-based decisioning for inventory planning and warehouse slotting, integrating solvers and forecasts into agentic workflows

Requirements

  • 8–12 years in software/AI engineering, including 2+ years leading teams or owning technical delivery as a hands-on lead
  • Demonstrated domain exposure in commerce/retail, e-commerce, fulfillment, logistics, or supply chain
  • Track record shipping AI/ML systems to production in enterprise or client-services settings
  • Strong communication and presence
  • Proven, hands-on experience designing and shipping enterprise-grade agentic AI systems to production
  • Deep expertise with agent frameworks (LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent)
  • Expert LLM integration: function/tool calling, structured outputs, retrieval-augmented generation (RAG), agent memory, and knowledge retrieval
  • Production hardening of agentic systems: agent evaluation and regression testing, guardrails and safety, observability/tracing, prompt and context management, and cost/latency optimization
  • Model Context Protocol (MCP): designing and deploying MCP servers for tool and resource integration
  • Human-in-the-loop and approval workflows for high-stakes autonomous actions in enterprise environments
  • Expert-level Python with strong software-engineering fundamentals, design, and code quality
  • Strong with FastAPI (and/or Django) for high-performance APIs and services
  • asynchronous programming (asyncio, async/await)
  • Solid ML foundations: scikit-learn, pandas, NumPy
  • familiarity with deep-learning frameworks (PyTorch/TensorFlow/Keras)
  • API design and documentation (OpenAPI/Swagger), web security and authentication (JWT, OAuth), and testing/TDD
  • Data stores and messaging: relational and NoSQL databases
  • message queues and event streaming (Apache Kafka, RabbitMQ)
  • Production experience on a major cloud (AWS, GCP, or Azure) and its AI/ML services
  • Containerization (Docker) and orchestration
  • CI/CD for building, testing, and deploying applications
  • Vector databases and RAG infrastructure
  • LLMOps / observability tooling (e.g., LangSmith or equivalent)
  • Git and collaborative, agile development at scale

Nice to have

  • OMS/WMS or supply-chain platform experience (order management, warehouse management, transportation/parcel)
  • Exposure to marketing/martech or finance-operations automation
  • Open-source contributions to agent/LLM tooling, or experience standing up an agentic platform or reusable framework
  • Working knowledge of mathematical / operations-research optimization: linear and integer programming (LP/MILP), constraint programming, and heuristic/metaheuristic methods
  • Hands-on with optimization solvers/libraries such as Google OR-Tools, Gurobi, CPLEX, or PuLP/Pyomo
  • Ability to model real-world supply-chain problems — inventory placement/replenishment and warehouse slotting — and embed optimization into agentic decisioning
  • Familiarity with demand forecasting and connecting predictive models to optimization and downstream automated action
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