Gen AI Engineer

Infosys•Houston, NJ

3d•Onsite

About The Position

In the assigned Job Role of Data Science Consultant 2, your Area Of Responsibility will be as below: Develop data preparation tasks, while identifying patterns or anomalies. Ensure data readiness for advanced modeling. Develop models for complex use cases (e.g., forecasting models, LLM-based solutions), while refining algorithms to meet business needs, and ensure smooth deployment into scalable, production-ready solutions. Conduct testing and optimize algorithms for performance, reliability, and scalability, while providing guidance to team members in best practices. Design and develop predictive models and data-driven analyses to address business challenges. Build, evaluate, and deploy models, standardize code, and contribute to knowledge management. Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions. Define analytics problems for projects; execute visualization, analysis, and predictive modeling under guidance. Proactively maintain models and implement improvements for accuracy and reliability. Apply governance controls to mitigate risks and ensure compliance. Analyze performance trends, recommend improvements, and document discrepancies for escalation. Maintain comprehensive documentation standards, while participating in knowledge transfer sessions. Participate in discussions with stakeholders to refine requirements, provide insights, and guide implementation of models. Apply the predefined quality measurement framework at an individual task level in the project. Deploy complex analytics tools or multi-system integration, while validating deployment success. Participate in developing scripts or templates for repeated deployments tasks. Contribute to analytic solutions, IP asset creation, and training initiatives. Contribute to thought leadership such as papers, innovative non-ML, ML, deep learning or LLM models, and proofs of concepts. Participate in and deliver analytics training, while contributing to content creation. Provide input for segment and unit-level business plans. Your contribution to the team: Deliver scalable, high-quality analytics solutions aligned to business needs. A knack for optimization, deployment and performance improvement of models. The ability to drive innovation through advanced analytics, automation and thought leadership. Enable team growth through knowledge sharing, training and standardization. Support business planning with data-driven insights. 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

Python and hands-on building of enterprise GenAI applications with Lang Chain, Lang Graph, Llama Index, or similar orchestration frameworks; comfortable with RAG, vector databases, agentic workflows (tool calling, memory, multi-agent), and prompt engineering.
Working with Azure OpenAI, AWS Bedrock, OpenAI, Anthropic, or similar LLM platforms; integrating with enterprise APIs, databases, and knowledge repositories.
Building production APIs and microservices with Fast API, Docker, and Kubernetes; software engineering fundamentals (system design, testing, CI/CD, Git); hands-on with AI coding assistants (GitHub Copilot, Claude Code, Cursor) for engineering productivity.
LLMOps practices — observability, tracing, evaluation (RAGAS, DeepEval, Lang Smith), guardrails, cost governance, and model safety.
Conducting code reviews, driving technical decisions, and collaborating with product and platform teams.
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.
Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply.
Infosys is unable to provide immigration sponsorship for this role now or in the future.

Nice To Haves

Open-source LLMs (Llama, Mistral, Gemma) and fine-tuning techniques (LoRA, QLoRA, PEFT); familiarity with Model Context Protocol (MCP).
Multimodal AI (vision-language, OCR, speech) and document intelligence.
Front-end (React, TypeScript), DevOps/IaC tooling (GitHub Actions, Terraform, Helm), and domain exposure across financial services, telecom, retail, or healthcare

Responsibilities

Develop data preparation tasks, while identifying patterns or anomalies.
Ensure data readiness for advanced modeling.
Develop models for complex use cases (e.g., forecasting models, LLM-based solutions), while refining algorithms to meet business needs, and ensure smooth deployment into scalable, production-ready solutions.
Conduct testing and optimize algorithms for performance, reliability, and scalability, while providing guidance to team members in best practices.
Design and develop predictive models and data-driven analyses to address business challenges.
Build, evaluate, and deploy models, standardize code, and contribute to knowledge management.
Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions.
Define analytics problems for projects; execute visualization, analysis, and predictive modeling under guidance.
Proactively maintain models and implement improvements for accuracy and reliability.
Apply governance controls to mitigate risks and ensure compliance.
Analyze performance trends, recommend improvements, and document discrepancies for escalation.
Maintain comprehensive documentation standards, while participating in knowledge transfer sessions.
Participate in discussions with stakeholders to refine requirements, provide insights, and guide implementation of models.
Apply the predefined quality measurement framework at an individual task level in the project.
Deploy complex analytics tools or multi-system integration, while validating deployment success.
Participate in developing scripts or templates for repeated deployments tasks.
Contribute to analytic solutions, IP asset creation, and training initiatives.
Contribute to thought leadership such as papers, innovative non-ML, ML, deep learning or LLM models, and proofs of concept.
Participate in and deliver analytics training, while contributing to content creation.
Provide input for segment and unit-level business plans.
Deliver scalable, high-quality analytics solutions aligned to business needs.
Drive innovation through advanced analytics, automation and thought leadership.
Enable team growth through knowledge sharing, training and standardization.
Support business planning with data-driven insights.