Gen AI Engineer

Overview

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 a pioneering team at the forefront of AI innovation, work with cutting‑edge technologies, collaborate with industry experts, and contribute to transformative projects that shape the future of business. The culture emphasizes continuous learning, growth, and a supportive environment for those passionate about AI.

Responsibilities

• Develop data preparation tasks, identify patterns or anomalies, and ensure data readiness for advanced modeling.
• Develop models for complex use cases (e.g., forecasting models, LLM‑based solutions), refine algorithms to meet business needs, and ensure smooth deployment into scalable, production‑ready solutions.
• Conduct testing and optimize algorithms for performance, reliability, and scalability; provide guidance to team members on 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, enhance 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 and participate 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, validate deployment success, and develop scripts or templates for repeated deployment 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.
• Deliver analytics training and contribute to content creation.
• Provide input for segment and unit‑level business plans.

Team Contributions

• Deliver scalable, high‑quality analytics solutions aligned to business needs.
• Optimize, deploy, and improve performance of models.
• Drive innovation through advanced analytics, automation, and thought leadership.
• Enable team growth via knowledge sharing, training, and standardization.
• Support business planning with data‑driven insights.

Required Skills and Experience

• Python and hands‑on building of enterprise GenAI applications with LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks; comfortable with RAG, vector databases, agentic workflows (tool calling, memory, multi‑agent), and prompt engineering.
• Experience with Azure OpenAI, AWS Bedrock, OpenAI, Anthropic, or similar LLM platforms; integration with enterprise APIs, databases, and knowledge repositories.
• Building production APIs and microservices using FastAPI, Docker, and Kubernetes; solid software‑engineering fundamentals (system design, testing, CI/CD, Git).
• Proficiency with AI coding assistants (GitHub Copilot, Claude Code, Cursor) to boost engineering productivity.
• LLMOps practices – observability, tracing, evaluation (RAGAS, DeepEval, LangSmith), guardrails, cost governance, and model safety.
• Conducting code reviews, driving technical decisions, and collaborating with product and platform teams.

Preferred Skills and Experience

• 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 development (React, TypeScript), DevOps/IaC tooling (GitHub Actions, Terraform, Helm).
• Domain exposure across financial services, telecom, retail, or healthcare.

Required Qualifications

Bachelor’s degree or foreign equivalent from an