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About This Role
Job Category: AI
Job Type: Full Time
Job Location: Atlanta\- United States Remote
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Acuver Consulting Private Limited, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Acuver Consulting Private Limited AI Hiring
Acuver Consulting Private Limited has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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