In this role, you will:

• Drive the creation of AI and machine learning solutions, covering end-to-end work such as data preparation, model training, fine-tuning, and deployment strategy development.
• Design and implement scalable data pipelines to curate, clean, and prepare both structured and unstructured data for machine learning initiatives and foundation model training.
• Develop and improve advanced agentic AI workflows using modern orchestration frameworks to enable intelligent automation and better decision-making.
• Architect state-of-the-art Retrieval-Augmented Generation (RAG) systems, including semantic search, vector embeddings, re-ranking approaches, and document retrieval optimization.
• Implement reliable evaluation and benchmarking frameworks to assess model accuracy, reliability, safety, and performance in partnership with cloud-native AI deployment efforts.

Work model

For Hybrid Roles

We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring [X] days a week in a client or Cognizant office in [Location]. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

What you need to have to be considered

• 10+ years of experience in Data Science, Machine Learning, or AI Engineering, including experience preparing and managing large-scale datasets.
• Expert-level proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Hands-on experience with large language models (LLMs), small language models (SLMs), prompt engineering, and agentic AI frameworks.
• Strong knowledge of vector databases, semantic search architectures, and SQL/NoSQL data platforms.
• Experience working with cloud AI platforms such as Azure AI Foundry, Vertex AI, or Amazon SageMaker, including model deployment and operationalization.

These will help you stand out

• Experience implementing advanced RAG architectures and enterprise search solutions.
• Expertise in model fine-tuning techniques such as LoRA, PEFT, and hyperparameter optimization.
• Experience establishing AI evaluation and benchmarking frameworks using tools such as RAGAS or TruLens.
• Knowledge of synthetic data generation, data labeling strategies, and responsible AI practices including bias mitigation.
• Experience deploying AI solutions in cloud-native and containerized environments across Azure, AWS, or Google Cloud Platform.