AI ML Engineer

Need to have a solid 3+ years experience

AI/ML Engineer – Core Systems & Intelligence Platform

We are looking for an AI/ML Engineer who can design and build the central intelligence layer that powers our data-driven products, automation workflows, and decision engines. This role goes beyond model building you will architect scalable systems that integrate data, machine learning, and business logic into a unified platform.

Role Overview

You will work closely with leadership, product, and engineering teams to convert business problems into intelligent systems. The focus is on building robust pipelines, deploying production-grade models, and creating a reusable AI backbone that supports multiple business use cases.

Key Responsibilities

• Design and build the “mother system” that integrates data pipelines, ML models, APIs, and business workflows into a unified architecture
• Develop end-to-end ML systems: data ingestion → preprocessing → model training → deployment → monitoring
• Work with structured and unstructured data across multiple business domains (supply chain, analytics, operations, etc.)
• Build and deploy scalable ML models (prediction, recommendation, classification, optimization)
• Create reusable AI services and APIs that can be leveraged across products
• Collaborate with product teams to translate business requirements into AI-driven solutions
• Optimize model performance, latency, and cost for production environments
• Implement MLOps best practices (CI/CD, model versioning, monitoring, retraining pipelines)
• Design data architecture and ensure data quality, governance, and security

Required Skills

• Strong experience in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Solid understanding of data engineering (ETL pipelines, data lakes, SQL/NoSQL databases)
• Experience with cloud platforms (Azure preferred, AWS/GCP acceptable)
• Hands-on experience in deploying ML models into production (APIs, microservices)
• Knowledge of MLOps tools and practices (Docker, Kubernetes, MLflow, Airflow, etc.)
• Strong problem-solving ability with a systems-thinking mindset
• Ability to work with cross-functional teams and translate business problems into technical solutions