Principal​/Lead AI-ML Engineer – Graphs & Generative AI

Principal / Lead AI-ML Engineer – Knowledge Graphs & Generative AI About the role

As a Principal / Lead AI-ML Engineer – Knowledge Graphs & Generative AI
, you will make an impact by designing and delivering enterprise-scale AI solutions that combine knowledge graphs, generative AI, and agentic systems to enable intelligent decision-making, contextual reasoning, and automation. You will be a valued member of the AI Engineering team and work collaboratively with data scientists, architects, product leaders, and business stakeholders to transform complex unstructured data into scalable, production-grade AI capabilities.

• Design and build enterprise knowledge graph solutions that enable semantic search, contextual intelligence, advanced analytics, and automated reasoning across large-scale unstructured data sources.
• Develop and deploy agentic AI systems that enrich, validate, and continuously improve knowledge repositories using LLMs, Vision‑Language Models (VLMs), and multimodal AI capabilities.
• Architect and implement AI/ML pipelines leveraging large language models, small language models, retrieval‑augmented generation (RAG), GraphRAG, and task‑specific AI models.
• Lead the development of scalable machine learning and graph‑based solutions that support anomaly detection, relationship discovery, semantic inference, and intelligent automation.
• Provide technical leadership and collaborate across engineering, product, and business teams to establish best practices, drive innovation, and deliver production‑ready AI platforms.

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 3 days per week in a Cognizant or client office in Dallas, TX, with Charlotte, NC as a secondary location option requiring time in a Cognizant or client office as determined by project and business needs.

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.

• 10+ years of hands‑on AI/ML engineering experience, including designing and deploying enterprise‑scale AI solutions in production environments.
• Deep expertise in knowledge graph technologies, semantic data modeling, ontology development, and graph‑based reasoning systems.
• Strong experience building and operationalizing agentic AI solutions, including multimodal applications leveraging Vision‑Language Models (VLMs).
• Advanced proficiency in Python and experience developing machine learning, AI, and data engineering pipelines.
• Hands‑on experience with large language models (LLMs), generative AI platforms, prompt engineering, fine‑tuning techniques, and retrieval‑augmented generation (RAG).
• Experience with graph technologies such as Neo4j, GraphDB, RDF, OWL, Cypher, SPARQL, and entity resolution methodologies.
• Proven ability to design, deploy, and scale AI systems using cloud platforms such as Azure, AWS, or Google Cloud Platform.
• Strong understanding of MLOps and LLMOps practices, including model deployment, observability, monitoring, governance, and performance optimization.

• Experience implementing GraphRAG architectures that combine knowledge graphs and generative AI for advanced reasoning and contextual intelligence.
• Expertise with agent orchestration frameworks such as Lang Chain, Lang Graph, Llama Index, or similar technologies.
• Experience with vector databases and semantic search technologies, including Pinecone, FAISS, or comparable platforms.
• Knowledge of anomaly detection, graph analytics, embeddings, and relationship inference techniques.
• Experience leading technical teams, mentoring engineers, and driving enterprise AI strategy and architecture.
• Strong background in building highly scalable distributed AI systems across complex business domains.