GenAI Engineer

Infosys•Houston, NC

About The Position

Infosys Topaz is an AI-first suite of services, solutions, and platforms designed to accelerate business value through generative AI technologies. It amplifies the potential of individuals, enterprises, and communities by fostering unprecedented innovations, pervasive efficiencies, and connected ecosystems. Leveraging Infosys' applied AI framework, Topaz empowers users to deliver cognitive solutions that drive growth, build interconnected ecosystems, and unlock efficiencies at scale. Join us to be part of a pioneering team at the forefront of AI innovation. At Infosys Topaz, you'll have the opportunity to work with cutting-edge technologies, collaborate with industry experts, and contribute to transformative projects that shape the future of business. We are committed to fostering a culture of continuous learning and growth, ensuring that our team members thrive in a dynamic and supportive environment. If you're passionate about AI and eager to make a significant impact, Infosys Topaz is the perfect place for you to grow and excel.

Requirements

Enterprise GenAI and Agentic AI solutions across RAG, AI agents, conversational AI, enterprise search, workflow automation, document intelligence, and AI copilots; comfortable with planner-executor, reflection, multi-agent, and graph-based orchestration patterns.
Hands-on with orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI) and vector databases (Pinecone, Weaviate, Milvus, pgvector, FAISS, ChromaDB, Azure AI Search); working knowledge of grounding, prompt engineering, and context management.
Experience integrating GenAI with Azure OpenAI, AWS Bedrock, Vertex AI, OpenAI, Anthropic, and Gemini, along with enterprise APIs, middleware, and data platforms.
Knowledge of AI governance, LLMOps, evaluation, observability, guardrails, model safety, compliance, and cloud-native deployment.
Bachelor’s degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
This position may require relocation and/or travel to work/project location.
All applicants authorized to work in the United States are encouraged to apply.

Nice To Haves

Exposure to open-source LLM ecosystems — Hugging Face, PyTorch, LoRA, QLoRA, PEFT — and models such as Llama, Mistral, Gemma, DeepSeek, and Falcon.
Familiarity with multimodal AI, including vision-language models, speech and audio models, and image or video generation.
Familiarity with DevOps and IaC tooling (GitHub Actions, Jenkins, Terraform, Helm, Kubernetes) and awareness of front-end stacks (React, Angular, TypeScript, GraphQL) used in copilot interfaces.

Responsibilities

Participate in data extraction, transformation, and preparation.
Resolve common data issues and ensure quality for model development.
Participate in developing models using statistical or machine learning techniques and collaborate with technology teams to operationalize them into analytics tools or scripts.
Participate in model testing and validation, selecting the best-performing algorithms based on statistical and business metrics.
Participate in the development of advanced analytics and machine learning or deep learning models including LLMs using predefined processes and tools like SAS and R/ Python.
Participate in defining analytics problems; execute visualization, analysis, and predictive modeling with senior support.
Identify data sources and extract from RDBMS and develop UI/UX for client usage.
Participate in model performance, while making minor adjustments, and escalate risks or compliance concerns and generate reports on deviations or schedule slippages.
Proactively participate in detailed documentation of model development, testing, and deployment activities for reproducibility.
Work closely with business and technology teams to translate requirements into actionable models, while communicating results effectively.
Apply predefined quality measurement frameworks, if any, to individual project tasks.
Participate in deploying analytics tools in test and production environment, while ensuring they meet operational requirements.