AI/ML Engineer (Optimization )

Overview

AI/ML Engineer

Key Responsibilities

• Design, develop, and deploy machine learning models for a variety of tasks, including

  regression, classification, and clustering

  .
• Research and implement

  optimization algorithms

  to solve complex problems, such as

  warehouse layout optimization, route optimization, and supply chain efficiency

  .
• Leverage expertise in

  Mathematical Optimization

  , including

  Mixed-Integer Programming (MIP)

  , using solvers and libraries such as

  PuLP

  ,

  Google OR-Tools

  , and commercial solvers like

  Gurobi

  and

  CPLEX

  .
• Apply a variety of

  metaheuristic algorithms

  , including

  Genetic Algorithms

  ,

  Simulated Annealing

  , and

  Tabu Search

  , to find efficient solutions for large-scale, combinatorial problems.
• Build and deploy

  GenAI solutions

  , including

  Retrieval-Augmented Generation (RAG)

  systems and

  Agentic AI

  frameworks, to enhance business processes and customer experiences.
• Collaborate with data scientists, product managers, and other engineers to understand business requirements and translate them into technical solutions.
• Maintain and improve existing machine learning and optimization pipelines, ensuring models are scalable, reliable, and performant.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field.
• Proven experience in solving

  classical machine learning problems

  (e.g., scikit-learn, XGBoost).
• Strong background in

  optimization research

  , with hands-on experience in mathematical and heuristic optimization.
• Expertise with

  Mixed-Integer Programming (MIP)

  and proficiency with tools like

  PuLP

  or

  Google OR-Tools

  , and familiarity with commercial solvers like

  Gurobi

  or

  CPLEX

  .
• Solid understanding and practical experience with various

  optimization algorithms

  such as

  Genetic Algorithms

  ,

  Simulated Annealing

  , and

  Tabu Search

  .
• Hands-on experience with

  GenAI solutions

  , particularly

  RAG

  and

  Agentic AI

  systems.
• Proficiency in Python and relevant libraries (e.g., Pandas, NumPy, TensorFlow, PyTorch).
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience in solving

  warehouse-related optimization problems

  (e.g., slotting, picking path optimization).
• Experience with

  route optimization

  algorithms and real-world applications.
• Familiarity with cloud platforms (AWS, GCP, Azure) for model deployment and management.