Manager-Data & ML Engg

Cognizant; Chennai District; On-site; INR 2,400,000 - 4,200,000; Full time

Job summary

Cognizant seeks an experienced ModelOps Lead to drive enterprise AI initiatives across ML and LLM platforms. You will own end-to-end AI lifecycle design, deployment, monitoring, governance, and optimization to deliver scalable business value.

You will lead cross-functional teams, establish ModelOps/LLMOps strategies, and ensure responsible AI, security, and compliance across large client engagements.

Qualifications

• 7+ years of overall IT experience with significant experience in AI Engineering, ModelOps, MLOps, or AI Platform leadership roles.
• Proven experience in deploying, governing, monitoring, and scaling ML and LLM-based solutions in enterprise environments.
• Strong client-facing consulting, stakeholder management, and delivery leadership experience.
• Experience leading cross-functional teams comprising AI Engineers, ML Engineers, Data Engineers, and Platform Engineers.
• BFSI or other regulated industry experience preferred.

Responsibilities

• Lead design, development, deployment, and operationalization of AI, Generative AI, RAG and Agentic AI solutions.
• Define and execute enterprise ModelOps, MLOps, and LLMOps strategies across AI platforms.
• Establish governance, monitoring, observability, and support frameworks for AI applications and models.
• Drive AI platform modernization, innovation, and continuous improvement initiatives.
• Deploy, monitor, evaluate, and optimize ML and GenAI models.
• Support development of AI powered business solutions leveraging Power BI, Power Apps, and Power Automate.
• Define and track model performance, explainability, fairness, governance, and business impact metrics.
• Build and manage CI/CD/CT pipelines for ML and LLM deployments.
• Implement model registries, experiment tracking, versioning, monitoring, and observability frameworks.
• Establish controls for model drift, data drift, hallucinations, prompt injection, toxicity, latency, and cost optimization.
• Implement Responsible AI, Model Risk Management, compliance, privacy, explainability, and governance controls.
• Act as a trusted advisor for client AI transformation programs and GenAI adoption initiatives.
• Lead architecture reviews, governance discussions, workshops, executive presentations, and solutioning activities.
• Support AI strategy engagements, estimations, and RFP responses.
• Manage delivery, resource planning, stakeholder communication, and capability-building initiatives.

Skills

Generative AI

LLMs

RAG Architectures

Agentic AI

Prompt Engineering

AI Agents

Azure OpenAI

Azure AI Foundry

LangChain

LangGraph

Semantic Kernel

MLOps

LLMOps

MLflow

Kubeflow

PromptFlow

Model Registries

AI Observability

AI Governance

Power BI

Power Apps

Power Automate

Azure DevOps

GitHub Actions

Docker

Kubernetes

Terraform

CI/CD

Python

SQL

REST APIs

PostgreSQL

Redis

Education

Bachelor’s or Master’s degree in Computer Science/Data Science/AI/Engineering

Tools

Databricks

Apache Spark

Kafka

Data Pipelines

Event-Driven Architectures

Real-Time Data Processing

Power BI

Power Apps

Power Automate

Azure DevOps

GitHub Actions

Docker

Kubernetes

Terraform

CI/CD

Job description

We are seeking an experienced ModelOps Lead to drive enterprise-scale AI, Generative AI, and ModelOps initiatives across Machine Learning and LLM platforms. The ideal candidate will have 7+ years strong hands-on expertise in AI Engineering, GenAI, RAG, Agentic AI, MLOps, LLMOps, AI Governance, and Observability, along with working knowledge of Data Engineering, Power Platform, Data Science, and DevOps practices. The role involves leading the end-to-end AI lifecycle, from solution design and development through deployment, monitoring, governance, and continuous optimization while ensuring scalability, reliability, compliance, and business value realization.AI Engineering & ModelOps Leadership

Responsibilities

• Lead the design, development, deployment, and operationalization of AI, Generative AI, RAG and Agentic AI solutions.
• Define and execute enterprise ModelOps, MLOps, and LLMOps strategies across AI platforms.
• Establish governance, monitoring, observability, and support frameworks for AI applications and models.
• Drive AI platform modernization, innovation, and continuous improvement initiatives.
• Deploy, monitor, evaluate, and optimize ML and GenAI models.
• Support development of AI powered business solutions leveraging Power BI, Power Apps, and Power Automate.
• Define and track model performance, explainability, fairness, governance, and business impact metrics.
• Build and manage CI/CD/CT pipelines for ML and LLM deployments.
• Implement model registries, experiment tracking, versioning, monitoring, and observability frameworks.
• Establish controls for model drift, data drift, hallucinations, prompt injection, toxicity, latency, and cost optimization.
• Implement Responsible AI, Model Risk Management, compliance, privacy, explainability, and governance controls.
• Act as a trusted advisor for client AI transformation programs and GenAI adoption initiatives.
• Lead architecture reviews, governance discussions, workshops, executive presentations, and solutioning activities.
• Support AI strategy engagements, estimations, and RFP responses.
• Manage delivery, resource planning, stakeholder communication, and capability-building initiatives.

Required Skills

• Strong expertise in Generative AI, LLMs, RAG Architectures, Agentic AI, Prompt Engineering, AI Agents, Azure OpenAI, Azure AI Foundry, LangChain, LangGraph, and Semantic Kernel.
• Hands-on experience with MLOps, LLMOps, MLflow, Kubeflow, PromptFlow, Model Registries, AI Observability, and AI Governance platforms.
• Good understanding of Machine Learning, NLP, Model Evaluation, Explainability, and Responsible AI concepts.
• Working knowledge of Databricks, Apache Spark, Kafka, Data Pipelines, Event-Driven Architectures, and Real-Time Data Processing.
• Experience building analytics and business applications using Power BI, Power Apps, and Power Automate.
• Familiarity with Azure DevOps, GitHub Actions, Docker, Kubernetes, Terraform, and CI/CD practices.
• Strong proficiency in Python, SQL, REST APIs, PostgreSQL, and Redis.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Engineering, or related field.
• 7+ years of overall IT experience with significant experience in AI Engineering, ModelOps, MLOps, or AI Platform leadership roles.
• Proven experience in deploying, governing, monitoring, and scaling ML and LLM-based solutions in enterprise environments.
• Strong client-facing consulting, stakeholder management, and delivery leadership experience.
• Experience leading cross-functional teams comprising AI Engineers, ML Engineers, Data Engineers, and Platform Engineers.
• BFSI or other regulated industry experience preferred.

Preferred Certifications

• Microsoft Azure AI Engineer Associate
• Azure Machine Learning
• AWS Machine Learning Specialty
• Kubernetes or Cloud Platform Certifications