Lead Machine Learning Engineer

Company Overview 

DataHat AI is building the next generation of intelligent decision systems for the fashion and retail ecosystem. We help global fashion brands and retailers optimize demand forecasting, inventory planning, replenishment, and omnichannel fulfillment using a combination of machine learning, deep learning, computer vision, and generative AI. 

Our platform goes beyond dashboards and static models—we design agentic. 

At DataHat AI, you’ll work at the intersection of real-world supply chain problems, cutting-edge AI, and high-impact business outcomes—with the freedom of a remote-first culture and the ambition of a product-led AI company. 

Role Overview 

We are looking for a Lead Machine Learning Engineer who can own end-to-end ML systems—from problem framing and feature design to model orchestration, evaluation, and production deployment. 

This role is ideal for someone who: 

• Loves complex, messy real-world data 
• Enjoys blending statistical ML, deep learning, and GenAI 
• Thinks in systems and workflows, not just models 
• Can translate business problems into scalable, explainable AI solutions 

You will play a key role in shaping how AI agents, forecasting models, and optimization engines are built and deployed across fashion retail, physical stores, and e-commerce. 

What You’ll Work On 

Forecasting That Actually Works 

• Build real-world demand forecasting systems for fashion using ML, deep learning, and statistical models. 
• Tackle messy problems like intermittent demand, stockouts, seasonality, and cold starts across stores and e-commerce. 
• Design segmented and ensemble models that balance accuracy, stability, and business risk. 

Data/Features Engineering, Not Guesswork 

• Design semantic data models that encode business meaning, hierarchies, and relationships 
• Create high-signal features from sales, pricing, inventory, calendars, weather, and store performance. 

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• Set data quality and imputation rules that prevent leakage, bias, and false insights. 
• Make models explainable and trustworthy, not black boxes. 

Agentic AI in Production 

• Build AI agents that reason, plan, and critique forecasts and replenishment decisions. 
• Use LLMs to orchestrate workflows, apply business constraints, and explain outcomes in plain language. 
• Turn model outputs into actionable decisions, not just numbers. 

Vision-Powered Fashion Intelligence 

• Use computer vision to extract visual signals from product images—similarity, style, warmth, and more. 
• Solve cold-start and substitution problems with visual embeddings. 
• Go beyond transactional data to understand why products sell. 

Ship ML That Is Secure and Scales 

• Own models from idea → production → monitoring. 
• Build systems that are fast, reliable, and battle tested. 
• Build systems that are secure, observable and auditable. 
• Work closely with product and engineering to turn AI into measurable business impact. 

What We’re Looking For 

Strong ML Foundations 

• 7–10 years of hands-on experience building applied ML systems (not just experiments). 
• Deep understanding of statistical ML, time-series forecasting, and model evaluation. 
• Fluent in Python and the modern data stack (Pandas, NumPy, scikit-learn). 

Modern AI Builder Mindset 

• Experience with deep learning and Transformers—and strong judgment on when not to use them. 

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• Hands-on exposure to Generative AI, LLMs, and agentic AI patterns (tool use, planning, critique). 
• Hands-on experience with frameworks like Langchain, LangGraph, AutoGen etc. 
• Comfortable blending ML + rules + reasoning to solve real business problems. 

Computer Vision & Representation Learning 

• Experience working with image embeddings, similarity search, and clustering. 
• Ability to extract meaningful signals from product images (style, structure, visual similarity). 
• Fashion or retail CV experience is a strong plus. 

Systems & Domain Thinking 

• Ability to reason about end-to-end systems, not isolated models. 
• Experience working on retail, fashion, supply chain, or e-commerce problems (preferred). 
• Experience working with large, noisy, real-world datasets and imperfect signals. 
• Strong intuition for constraints, trade-offs, and operational realities. 

Leadership, Ownership & Impact 

• Demonstrated ability to own ambiguous problems, define success metrics, and drive solutions to production. 
• Strong communication skills—able to explain complex models, assumptions, and limitations clearly to technical and non-technical stakeholders. 
• Track record of influencing product, engineering, and business decisions through data and models. 
• Bias toward shipping, learning, and iterating over perfect theory. 
• Mentorship mindset and willingness to raise the technical bar of the team. 

Why Join Us 

• Solve real, high-impact business problems with AI 
• Work on GenAI + Agents + ML + CV in production, not just experiments 
• Build end-to-end systems, not isolated models 
• Fully remote-first, outcome-driven culture 
• High ownership, high learning, high impact