About Acuver
At Acuver Consulting Pvt. Ltd., we’re redefining how supply chains operate — helping global enterprises become faster, smarter, and more resilient. Founded in 2013 and headquartered in New Delhi, we are one of India’s fastest-growing players in the supply chain tech space.
Our strength lies in four core areas: Strategic Consulting, Enterprise Solutions, Bespoke Development, and Integration Services. Whether it’s implementing enterprise-grade OMS and WMS solutions or building custom AI-powered tools, we focus on delivering outcomes that matter — agility, efficiency, and long-term growth.
With a sharp focus on innovation and a people-first culture, we’ve earned the trust of Fortune 500 clients and industry accolades including Great Place to Work®, the India 5000 Best MSME Award, and inclusion in Forbes India Select 200.
At Acuver, we don’t just solve supply chain challenges — we build intelligent, future-ready solutions that help businesses stay ahead. If you’re looking to work where impact meets innovation, Acuver is the place to be.
We are a product company building an AI Orchestration Platform, delivering SaaS solutions to organizations worldwide. Our platform brings AI and modern integration capabilities to help customers resolve new-age automation and AI challenges.
The Role
Acuver Consulting is looking for a Technical Lead – AI/ML to lead the design and delivery of enterprise-grade agentic AI systems for our clients in commerce, retail, fulfillment, and supply chain. This is a hands-on leadership role: you will set the technical direction for a focused engineering pod, architect and build production agentic pipelines, and stay close to the code while mentoring a small team.
Because we run fast-moving, outcome-driven engagements, you must bring deep domain exposure from day one — contributing to client conversations, discovery, and solution architecture immediately rather than ramping up over months.
You will lead an automation program spanning fulfillment, commerce, sales, marketing, and finance workflows — building agents that take real action across order management, fulfillment and transportation, customer and client resolution, media and campaign planning, and back-office finance. A later phase of the program centers on optimization-driven decisioning for inventory planning and warehouse slotting, so applied optimization experience is a strong advantage.
Location – USA (Remote)
Key Responsibilities
- 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.
Key Skills
Leadership & Domain Expertise
- 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 — able to engage client SMEs and architect solutions.
- Track record shipping AI/ML systems to production in enterprise or client-services settings; consulting or customer-facing delivery experience strongly preferred.
- Strong communication and presence: can lead discovery workshops, present architecture and trade-offs to executives, and mentor engineers.
Agentic AI & Enterprise Pipelines
- 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) and orchestrating single- and multi-agent workflows.
- 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.
Core Technical & Backend
- 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).
Cloud, MLOps & Delivery
- 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.
What Success Looks Like in the First 90 Days
- Embedded with client stakeholders, owning the architecture for at least one production agentic workflow.
- A small pod operating to your technical standards, with evaluation, observability, and guardrails in place.
- A clear, validated path defined for agentic workflows for maximum impact and value realization to customers.
