Senior Data Scientist – Generative AI & LLM Engineering

Cognizant is seeking a Senior Data Scientist to design, tune, evaluate, optimize, and deploy enterprise-scale AI/ML solutions, with a focus on Generative AI and large language models. The role also contributes to AI architecture and develops production-ready solutions and pipelines across enterprise environments.

Responsibilities:

• Perform advanced Exploratory Data Analysis (EDA) and statistical analysis to identify patterns, relationships, anomalies, and actionable business insights
• Design, develop, evaluate, and deploy machine learning models for predictive analytics, classification, regression, forecasting, and other enterprise use cases
• Build Generative AI and LLM-based solutions, including RAG applications, AI agents, enterprise search, and knowledge-driven AI systems
• Fine-tune, profile, benchmark, and optimize LLMs for model quality, latency, accuracy, scalability, and cost efficiency
• Design model evaluation frameworks covering performance, relevance, hallucination, safety, robustness, and business effectiveness
• Contribute to end-to-end AI/ML architecture across cloud and on-premises environments, including model serving, data pipelines, and integration patterns
• Develop and operationalize MLOps/LLMOps pipelines for experimentation, versioning, deployment, monitoring, and continuous model improvement
• Evaluate AI/ML frameworks, foundation models, vector databases, and emerging technologies based on business and technical requirements
• Collaborate with data engineers, AI engineers, architects, product teams, and business stakeholders to translate requirements into scalable AI solutions
• Ensure AI solutions follow enterprise security, data governance, responsible AI, and production-readiness standards

Requirements:

• 6+ years of experience in Data Science, Machine Learning, AI Engineering, or Advanced Analytics roles
• Expert knowledge of Exploratory Data Analysis, statistical methods, feature engineering, machine learning algorithms, and model evaluation techniques
• 3+ years of strong hands-on Machine Learning experience developing and deploying production-grade models
• Hands-on experience with Generative AI and LLMs, including prompt engineering, RAG, embeddings, vector databases, model tuning, evaluation, and performance optimization
• Strong proficiency in Python and ML/AI frameworks such as Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, or equivalent technologies
• Experience with LLM fine-tuning approaches, model benchmarking, inference optimization, and responsible AI practices
• Familiarity with AI agents, multi-agent systems, MLOps/LLMOps, model monitoring, and enterprise AI platform architecture
• Experience with major cloud AI/ML platforms such as Azure, AWS, or GCP and production deployment practices
• Strong analytical, problem-solving, communication, and stakeholder collaboration skills with the ability to translate technical results into business outcomes