Manager-Data And ML Engg

Cognizant; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Cognizant in India seeks an experienced ModelOps Lead to drive enterprise-scale AI and GenAI programs across MLOps, LLMOps, and AI governance. You will design, deploy, monitor, and optimize AI solutions, ensuring reliability, compliance, and business value.

Responsibilities include building CI/CD for ML/LLM, model registries, observability, data and model drift controls, and stakeholder governance. You will lead cross-functional teams and engage with clients on AI transformation initiatives.

Qualifications

• 7+ years of IT experience with AI Engineering and ModelOps.
• Experience deploying and scaling ML/LLM solutions in enterprise environments.
• Client-facing consulting and delivery leadership.
• Experience leading cross-functional AI/ML/Data/Platform teams.
• BFSI or 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.
• 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 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

GenAI, LLMs, RAG Architectures

Agentic AI, Prompt Engineering

MLOps, LLMOps, AI Observability

Azure OpenAI, Azure AI Foundry

Power Platform, Power BI, Power Apps

Python, SQL, REST APIs

DevOps practices

Education

Bachelor’s or Master’s degree in CS/DS/AI

Tools

Databricks

Apache Spark

Kafka

Terraform

Docker

Kubernetes

MLflow

LangChain

LangGraph

Semantic Kernel

GitHub Actions

Azure DevOps

Power BI

Power Apps

Power Automate

Job description

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.

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