Team Lead - AI/ML

Merit Data And Technology; Chennai District; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Merit Data And Technology in Chennai is seeking a Senior Team Lead to drive end-to-end AI/ML initiatives across the organization, combining strategic thinking with hands-on engineering leadership. You will mentor engineers, shape solution architectures, and partner with product and client stakeholders to deliver scalable, high-impact ML solutions.

Lead model lifecycle from research through deployment, ensure governance and security, and guide cross-functional teams across data science, software,

Qualifications

• 8–12+ years of experience in AI/ML engineering, with at least 2+ years in a technical leadership or architectural role.
• Strong expertise in machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face, etc.
• Proven experience deploying ML models in production using MLOps frameworks (Kubeflow, MLflow, Airflow, Vertex AI, SageMaker, or equivalent).
• Strong programming expertise in Python and proficiency with distributed computing frameworks (Spark, Ray, Databricks, etc.).
• Experience with cloud AI environments (AWS, GCP, Azure).
• Excellent communication, stakeholder handling, and leadership skills.

Responsibilities

• Lead the design and architecture of scalable machine learning systems, pipelines, and infrastructure across multiple product lines.
• Evaluate and select appropriate ML frameworks, tools, and platforms based on business and technical requirements.
• Drive innovation in MLOps, automation, monitoring, and optimization to deliver reliable and continuously improving ML systems.
• Perform high-level code reviews, enforce best practices, and provide guidance on patterns and frameworks.
• Oversee model lifecycle from research, experimentation, training, and validation to deployment and monitoring.
• Own ML solution roadmaps and coordinate delivery across engineering, data science, product, and DevOps teams.
• Define and review project milestones, success metrics, and performance guidelines.
• Ensure ML models meet security, scalability, compliance, and governance standards.
• Identify risks proactively and drive mitigation strategies to ensure smooth execution and delivery.
• Manage, mentor, and upskill ML engineers, data scientists, and cross-functional teams.
• Support hiring, onboarding, and capability building for the growing ML team.
• Facilitate technical workshops, knowledge-sharing sessions, and performance reviews.
• Foster a collaborative, inclusive, and innovation-driven engineering culture.
• Collaborate with clients, business leaders, and product owners to translate business requirements into actionable ML solutions.
• Communicate ML project plans, results, challenges, and recommendations to both technical and non-technical audiences.
• Represent the ML practice in executive-level discussions, solution presentations, and customer engagements.
• Stay current with emerging AI/ML trends, frameworks, and research advancements.
• Evaluate and implement modern models including foundation models, generative AI, NLP, CV, and reinforcement learning where applicable.
• Promote reusable ML components, patterns, frameworks, and engineering excellence across the organization.

Skills

AI Leadership

ML Architecture

Python

Distributed Computing

MLOps

Cloud Platforms

Communication

Tools

TensorFlow

PyTorch

Scikit-learn

Hugging Face

Kubeflow

MLflow

Airflow

Vertex AI

SageMaker

Spark

Ray

Databricks

Job description

We are seeking a highly skilled Senior Team Lead to lead end-to-end advanced AI/ML initiatives across the organization. This role goes beyond technical execution—requiring strategic thinking, solution architecture, cross-functional leadership, and direct engagement with clients and business stakeholders. The Principal ML Engineer will provide technical leadership, drive innovation, mentor engineering teams, and ensure scalable and high-impact delivery of machine learning solutions.

Key Responsibilities

Technical Leadership & Architecture

• Lead the design and architecture of scalable machine learning systems, pipelines, and infrastructure across multiple product lines.
• Evaluate and select appropriate ML frameworks, tools, and platforms based on business and technical requirements.
• Drive innovation in MLOps, automation, monitoring, and optimization to deliver reliable and continuously improving ML systems.
• Perform high-level code reviews, enforce best practices, and provide guidance on patterns and frameworks.
• Oversee model lifecycle from research, experimentation, training, and validation to deployment and monitoring.

Project & Delivery Ownership

• Own ML solution roadmaps and coordinate delivery across engineering, data science, product, and DevOps teams.
• Define and review project milestones, success metrics, and performance guidelines.
• Ensure ML models meet security, scalability, compliance, and governance standards.
• Identify risks proactively and drive mitigation strategies to ensure smooth execution and delivery.

Team Leadership & People Management

• Manage, mentor, and upskill ML engineers, data scientists, and cross-functional teams.
• Support hiring, onboarding, and capability building for the growing ML team.
• Facilitate technical workshops, knowledge-sharing sessions, and performance reviews.
• Foster a collaborative, inclusive, and innovation-driven engineering culture.

Client & Stakeholder Management

• Collaborate with clients, business leaders, and product owners to translate business requirements into actionable ML solutions.
• Communicate ML project plans, results, challenges, and recommendations to both technical and non-technical audiences.
• Represent the ML practice in executive-level discussions, solution presentations, and customer engagements.

Research & Thought Leadership

• Stay current with emerging AI/ML trends, frameworks, and research advancements.
• Evaluate and implement modern models including foundation models, generative AI, NLP, CV, and reinforcement learning where applicable.
• Promote reusable ML components, patterns, frameworks, and engineering excellence across the organization.

Required Skills and Experience

• 8–12+ years of experience in AI/ML engineering, with at least 2+ years in a technical leadership or architectural role.
• Strong expertise in machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face, etc.
• Proven experience deploying ML models in production using MLOps frameworks (Kubeflow, MLflow, Airflow, Vertex AI, SageMaker, or equivalent).
• Strong programming expertise in Python and proficiency with distributed computing frameworks (Spark, Ray, Databricks, etc.).
• Experience with cloud AI environments (AWS, GCP, Azure).
• Excellent communication, stakeholder handling, and leadership skills.