Data Scientist – ML / GenAI / LLM

Cognizant; Bentonville (AR); On-site; USD 150,000 - 155,000; Full time

Benefits offered by this job

Medical Insurance

Paid Time Off

401(k) Plan

Disability Insurance

Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant is seeking an experienced Data Scientist to lead production-grade ML and GenAI initiatives. You will work across the ML lifecycle from problem framing to deployment, with a strong emphasis on building GenAI/LLM-based applications and enterprise AI use cases.

Ideal candidates have 10+ years of experience, deep knowledge of ML engineering, and hands-on expertise with Python, ML frameworks, and cloud platforms.

Qualifications

• Strong hands-on experience in Data Science and Machine Learning.
• Strong programming skills in Python.
• Experience developing and productionizing ML models and ML pipelines.
• Strong understanding of Machine Learning Engineering (MLE) concepts.
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Experience with prompt engineering, RAG, embeddings, vector search, and LLM evaluation.
• Understanding of transformer architectures, NLP, and foundation models.
• Experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or similar.
• Experience integrating LLMs into enterprise applications.
• Working knowledge of MLOps, model deployment, monitoring, versioning, and CI/CD concepts.
• Experience with cloud data/AI platforms such as AWS, Azure, or GCP.
• Strong understanding of statistics, experimentation, feature engineering, model selection, and evaluation.

Responsibilities

• Design, develop, and implement advanced ML and Data Science solutions for complex business problems.
• Build, train, evaluate, and optimize ML models using structured and unstructured data.
• Develop Generative AI and LLM-based applications, including conversational AI and knowledge assistants.
• Work with pre-trained LLMs and apply prompt engineering, RAG, fine-tuning, and evaluation.
• Design and implement Retrieval-Augmented Generation (RAG) solutions leveraging enterprise data sources.
• Build robust data and ML pipelines to support model training, inference, and production deployment.
• Apply strong MLE principles to ensure models and AI applications are scalable, reliable, maintainable, and production-ready.
• Develop solutions using Python and commonly used ML/AI frameworks and libraries.
• Experiment with different models, architectures, prompts, retrieval strategies, and evaluation techniques.
• Implement appropriate LLM evaluation and monitoring approaches for accuracy, relevance, reliability, and performance.
• Collaborate with engineering, product, data, and business teams to translate business requirements into AI/ML solutions.
• Stay current with evolving developments in Generative AI, LLMs, Machine Learning, and MLOps.

Skills

Data Science and Machine Learning

Python

ML models and pipelines

Machine Learning Engineering (MLE)

Generative AI and LLMs

prompt engineering, RAG, embeddings, V

transformer architectures, NLP

ML frameworks: PyTorch, TensorFlow, sc

LLMs integration into enterprise apps

MLOps

AWS, Azure, GCP

Statistics and model evaluation

Tools

LangChain

LlamaIndex

Job description

Job Title: Data Scientist – ML / GenAI / LLM

Job Summary

We are looking for an experienced Data Scientist with strong expertise in Data Science, Machine Learning, Machine Learning Engineering (MLE), Generative AI, and Large Language Models (LLMs). The ideal candidate will have a strong foundation in building production-grade machine learning solutions and hands‑on experience developing GenAI/LLM-based applications.

This role requires someone who can work across the ML lifecycle, from problem formulation and model development to evaluation, deployment, optimization, and productionization.

**Must have 10+ years of experience**

Key Responsibilities

• Design, develop, and implement advanced Machine Learning and Data Science solutions for complex business problems.
• Build, train, evaluate, and optimize ML models using structured and unstructured data.
• Develop Generative AI and LLM-based applications, including conversational AI, knowledge assistants, summarization, information extraction, and other enterprise AI use cases.
• Work with pre-trained LLMs and apply techniques such as prompt engineering, RAG, fine-tuning, and model evaluation.
• Design and implement Retrieval-Augmented Generation (RAG) solutions leveraging enterprise data sources.
• Build robust data and ML pipelines to support model training, inference, and production deployment.
• Apply strong MLE principles to ensure models and AI applications are scalable, reliable, maintainable, and production-ready.
• Develop solutions using Python and commonly used ML/AI frameworks and libraries.
• Experiment with different models, architectures, prompts, retrieval strategies, and evaluation techniques to improve solution quality.
• Implement appropriate LLM evaluation and monitoring approaches for accuracy, relevance, reliability, and performance.
• Collaborate with engineering, product, data, and business teams to translate business requirements into AI/ML solutions.
• Stay current with evolving developments in Generative AI, LLMs, Machine Learning, and MLOps.

Required Skills

• Strong hands‑on experience in Data Science and Machine Learning.
• Strong programming skills in Python.
• Experience developing and productionizing ML models and ML pipelines.
• Strong understanding of Machine Learning Engineering (MLE) concepts.
• Hands‑on experience with Generative AI and Large Language Models (LLMs).
• Experience with prompt engineering, RAG, embeddings, vector search/vector databases, and LLM evaluation.
• Understanding of transformer architectures, NLP, and foundation models.
• Experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or similar technologies.
• Experience integrating LLMs/foundation models into enterprise applications.
• Working knowledge of MLOps, model deployment, monitoring, versioning, and CI/CD concepts.
• Experience working with cloud-based data and AI platforms such as AWS, Azure, or GCP.
• Strong understanding of statistics, experimentation, feature engineering, model selection, and model evaluation.

Preferred Skills

• Experience with LLM orchestration/frameworks such as LangChain, LlamaIndex, or similar tools.
• Experience with vector databases and semantic search solutions.
• Knowledge of AI agents / Agentic AI and tool-calling architectures.
• Experience building enterprise‑grade GenAI applications.
• Familiarity with LLM guardrails, responsible AI, security, and governance considerations.
• Experience optimizing GenAI solutions for latency, scalability, reliability, and cost.

Salary and Other Compensation:

The annual salary for this position is between $150-155Kdepending on experience and other qualifications of the successful candidate.

This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.

Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:

• Medical/Dental/Vision/Life Insurance
• Paid holidays plus Paid Time Off
• 401(k) plan and contributions
• Long-term/Short-term Disability
• Paid Parental Leave
• Employee Stock Purchase Plan

Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable la