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.
• 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 .
• 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