Generative AI Engineer

Cognizant Technology Solutions•Hartford, CT

36d•$130,000 - $150,000•Hybrid

About The Position

As a Generative AI Engineer, you will make an impact by designing, deploying, and optimizing AI-driven solutions that leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. You will be a valued member of the AI & Data Engineering team and work collaboratively with product managers, data scientists, and cloud engineers to deliver cutting-edge solutions. In this role, you will: Deploy and maintain knowledge ingestion pipelines and integrate them into API-based services. Fine-tune and deploy LLMs for enterprise-scale applications. Implement data cleansing, NLP techniques, and optimization strategies for RAG systems. Design and build solutions in cloud environments such as Azure, AWS, or GCP. Ensure quality through DevOps pipelines, automated testing, and CI/CD practices. Work model We strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring 3 days per week in a Cognizant or client office in Hartford, CT. Regardless of your working arrangement, we support a healthy work-life balance through our wellbeing programs. We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.

Requirements

5+ years of experience programming with Python.
1+ years of experience with Git and version control.
1+ years of experience with public cloud platforms (Azure, AWS, or GCP).
Hands-on experience with LangChain and building RAG systems.
Experience with vector databases such as MongoDB Atlas or Pinecone.
2+ years of NLP experience using tools like NLTK, SpaCy, and BeautifulSoup.
Familiarity with LLM concepts such as agents, memory, routing, and content moderation.
Strong debugging and code refactoring skills.

Nice To Haves

Experience deploying LLM-based RAG systems at scale (10,000+ documents).
Knowledge of Kubernetes and microservices architecture.
Experience with DevOps tools such as GitHub Actions or similar CI/CD platforms.
Portfolio of LLM applications and sample projects.
Understanding of advanced GenAI trends, frameworks, and best practices.

Responsibilities

Deploy and maintain knowledge ingestion pipelines and integrate them into API-based services.
Fine-tune and deploy LLMs for enterprise-scale applications.
Implement data cleansing, NLP techniques, and optimization strategies for RAG systems.
Design and build solutions in cloud environments such as Azure, AWS, or GCP.
Ensure quality through DevOps pipelines, automated testing, and CI/CD practices.