Applied AI/ML Lead

February 5, 2026

Job Overview

  • Date Posted
    February 5, 2026
  • Company Location
  • Expiration date
    March 13, 2026
  • Gender
    Both
  • Qualification
    B.Tech (Bachelor of Technology)
  • Career Level
    Manager

Job Description

Applied AI/ML Lead
Location: Remote (Candidate preferred from Pune)
Experience: 8+ Years

Position Overview
We are looking for a highly skilled Applied AI/ML Lead to drive the development and deployment of AI/ML solutions that deliver real business impact. This role focuses on transforming AI research into scalable, production-ready systems while leading and mentoring a growing AI/ML team.

You will collaborate with cross-functional teams to influence business strategy, improve customer experience, and implement advanced AI-driven personalization solutions.

Roles & Responsibilities

Lead end-to-end development and deployment of AI/ML models for customer-facing applications

Drive personalization using clustering, segmentation, and scoring techniques

Collaborate with Data Analytics, Engineering, and Product teams for production integration

Design and deliver AI-powered features such as recommendation systems and predictive analytics

Ensure data readiness and quality in collaboration with data engineering teams

Implement AIOps practices to monitor model performance and reduce MTTR

Mentor and guide AI/ML engineers while staying updated with the latest AI advancements

Must-Have Skills

8+ years of AI/ML experience with 3+ years in a leadership/technical lead role

Strong experience in applying AI/ML to real-world business problems

Hands-on expertise with TensorFlow, PyTorch, scikit-learn

Experience with AWS AI/ML services (SageMaker preferred)

Deep understanding of personalization, segmentation, scoring & recommendation systems

Strong knowledge of data pipelines and real-time systems (Kafka)

Proficient in Python for model development and deployment

Good to Have

Experience with MinIO, S3 for large-scale data management

Workflow automation using Airflow

Exposure to LLMs & RAG (Retrieval-Augmented Generation)

Qualification

Bachelor’s degree in Computer Science, Engineering, or a related technical field