Gen AI Engineer

Infosys•Charlotte, NC

2d•Onsite

About The Position

The Infosys Data and Analytics (DNA) unit is at the forefront of transforming data into actionable insights, driving business growth and operational efficiency. We specialize in leveraging advanced AI and analytics to create innovative solutions that address complex business challenges. Our team is dedicated to pioneering the future of data-driven decision-making, enabling organizations to unlock new opportunities and achieve sustainable success. Join us to be part of a dynamic team that is revolutionizing the way businesses harness the power of data and AI. At Infosys DNA, 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, the Infosys DNA unit is the perfect place for you to grow and excel.

Requirements

Experience in Generative AI and Large Language Models (LLMs)
Hands-on experience with agentic frameworks (multi-agent systems, tool-using agents, planners)
Solid understanding of: RAG architectures
Prompt engineering & prompt chaining
Conversational AI design patterns
Strong programming experience in Python preferred
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

Experience with LangChain / similar orchestration frameworks
Exposure to LLM fine-tuning, embeddings, vector databases
Experience building enterprise copilots or AI assistants
Familiarity with cloud-based GenAI platforms

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.