Gen AI Engineer

Infosys • Houston, NJ

1d

About The Position

In the assigned job role of Data Science Consultant 2, the Gen AI Engineer develops data preparation tasks, identifies patterns and anomalies, and ensures data readiness for advanced modeling. The role designs, builds, tests, and deploys predictive models and complex analytics solutions—including forecasting models and LLM-based applications—and refines algorithms to meet business needs. It also involves maintaining models, improving accuracy and reliability, and ensuring solutions are scalable and production-ready.

The engineer uses SAS, R, and Python to create reusable customizations for non-ML, machine learning, and deep learning algorithms, including LLM solutions. The role applies governance and quality controls, analyzes performance trends, documents discrepancies, and maintains comprehensive technical documentation. It includes stakeholder discussions to refine requirements, deployment validation, development of reusable deployment scripts and templates, and contributions to analytic solutions, intellectual property, thought leadership, proofs of concept, and training.

As part of Infosys Topaz, an AI-first suite of services, solutions, and platforms, the role contributes to generative AI projects intended to create business value. Team members work with advanced technologies and industry experts in a learning-focused environment. The position may require relocation and/or travel to the work or project location. Applicants must be authorized to work in the United States without employer-based visa sponsorship; Infosys states it cannot provide immigration sponsorship for this role now or in the future.

Requirements

• Python experience and hands-on experience building enterprise GenAI applications with LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks.
• Experience with RAG, vector databases, agentic workflows (including tool calling, memory, and multi-agent workflows), and prompt engineering.
• Experience working with Azure OpenAI, AWS Bedrock, OpenAI, Anthropic, or similar LLM platforms.
• Experience integrating AI solutions with enterprise APIs, databases, and knowledge repositories.
• Experience building production APIs and microservices with FastAPI, Docker, and Kubernetes.
• Software engineering fundamentals, including system design, testing, CI/CD, and Git.
• Hands-on experience with AI coding assistants such as GitHub Copilot, Claude Code, or Cursor.
• Knowledge of LLMOps practices, including observability, tracing, evaluation, guardrails, cost governance, and model safety.
• Bachelor’s degree or foreign equivalent from an accredited institution; alternatively, three years of progressive specialty experience may be considered in lieu of each year of education.
• Authorization to work in the United States for any employer without employer-based visa sponsorship.

Nice To Haves

• Experience with open-source LLMs such as Llama, Mistral, or Gemma.
• Familiarity with fine-tuning techniques such as LoRA, QLoRA, or PEFT.
• Familiarity with Model Context Protocol (MCP).
• Experience with multimodal AI, including vision-language, OCR, or speech.
• Experience with document intelligence.
• Front-end experience with React and TypeScript.
• Experience with DevOps or infrastructure-as-code tools such as GitHub Actions, Terraform, or Helm.
• Domain exposure in financial services, telecom, retail, or healthcare.

Responsibilities

• Develop data preparation tasks, identify patterns or anomalies, and ensure data readiness for advanced modeling.
• Develop and refine models for complex use cases, including forecasting and LLM-based solutions, and support deployment as scalable, production-ready solutions.
• Test and optimize algorithms for performance, reliability, and scalability, and guide team members in best practices.
• Design predictive models and data-driven analyses to address business challenges.
• Build, evaluate, and deploy models; standardize code; and contribute to knowledge management.
• Use SAS, R, and Python to create reusable customizations for non-ML, ML, and deep learning algorithms, including LLM-enhanced analytics.
• Define analytics problems and, under guidance, perform visualization, analysis, and predictive modeling.
• Maintain models and implement improvements to 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 and participate in knowledge transfer sessions.
• Engage with stakeholders to refine requirements, provide insights, and guide model implementation.
• Apply the predefined quality measurement framework to individual project tasks.
• Deploy complex analytics tools or multi-system integrations and validate deployment success.
• Participate in developing scripts or templates for repeated deployment tasks.
• Contribute to analytic solutions, intellectual property asset creation, and training initiatives.
• Contribute to thought leadership, including papers, innovative analytics models, and proofs of concept.
• Deliver analytics training and contribute to training content creation.
• Provide input for segment- and unit-level business plans.
• Build enterprise GenAI applications and integrate them with enterprise APIs, databases, and knowledge repositories.
• Build production APIs and microservices and apply LLMOps practices such as observability, tracing, evaluation, guardrails, cost governance, and model safety.
• Conduct code reviews, drive technical decisions, and collaborate with product and platform teams.

Benefits

• Medical, dental, vision, and life insurance.
• Long-term and short-term disability coverage.
• Health and dependent care reimbursement accounts.
• Accident, critical illness, hospital indemnity, and legal insurance.
• 401(k) plan and contributions dependent on salary level.
• Paid holidays and paid time off.
• Competitive pay.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level