Senior GenAI Engineer

Infosys; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Infosys in Bengaluru, India is hiring for an AI Platform Engineer focused on GenAI and LLM-based solutions within the Data Analytics Unit. You will design and deploy enterprise GenAI apps, build RAG pipelines, and integrate vector stores for efficient retrieval.

The role emphasizes scalable data pipelines, MLOps/LLMOps, and collaborative development with business stakeholders. You will mentor junior engineers, lead design reviews, and drive best practices for GenAI development, governance, and

Qualifications

• Bachelor's degree in Engineering or related fields (e.g., BCS, BBA, BCom, MCA, MSc).
• Experience with AI platform engineering and GenAI workflows is preferred.
• Proficiency in building scalable data pipelines and AI services with cloud platforms.

Responsibilities

• Generative AI development for enterprise GenAI applications using LLMs.
• Design and implement RAG pipelines, vector databases, and retrieval systems.
• Develop multi-agent workflows and agentic AI systems using LangGraph.
• Build conversational AI, copilots, knowledge assistants, and intelligent automation solutions.
• Engineer prompts and workflows to optimize model performance and UX.
• Deployment productionization, CI/CD for AI/ML workloads, monitoring and governance.
• Mentor junior engineers and participate in design reviews and architecture discussions.

Job description

Educational Requirements

• Bachelor of Engineering,BCS,BBA,BCom,MCA,MSc

Service Line

Data Analytics Unit

Responsibilities

• Generative AI Development
• Design and implement enterprise GenAI applications using LLMs.
• Develop multi-agent workflows and agentic AI systems using LangGraph.
• Build conversational AI, copilots, knowledge assistants, and intelligent automation solutions.
• Engineer prompts and workflows to optimize model performance and user experience.
• RAG Knowledge Systems
• Design and implement Retrieval-Augmented Generation (RAG) pipelines.
• Integrate vector databases and document retrieval frameworks.
• Optimize document chunking, embeddings, indexing, retrieval, and reranking strategies.
• Improve response accuracy, latency, and relevance of AI systems.

Additional Responsibilities

• Deployment Productionization
• Deploy and serve models using MLflow and Databricks.
• Monitor model performance, drift, and operational metrics.
• Implement CI/CD pipelines for AI and machine learning workloads.
• Ensure scalability, reliability, and observability of AI services.
• Leadership Mentorship
• Mentor junior engineers and AI practitioners.
• Conduct design reviews and architecture discussions.
• Define GenAI development standards and best practices.
• Collaborate with business stakeholders to identify AI use cases and translate them into solutions.

Technical and Professional Requirements

• AI Platform Engineering
• Build scalable AI platforms on Databricks and cloud environments.
• Develop model lifecycle management solutions using MLflow.
• Implement model tracking, versioning, deployment, monitoring, and governance.
• Support MLOps and LLMOps initiatives.
• Data Engineering
• Build and optimize large-scale data pipelines using PySpark.
• Process structured and unstructured data for AI and analytics workloads.
• Collaborate with data engineering teams to build feature and knowledge pipelines.
• Ensure data quality, lineage, and governance standards.

Preferred Skills

• Retrieval-Augmented Generation (RAG)
• LangGraph
• LLMs
• Generative AI
• Databricks
• MLflow
• PySpark
• CI/CD
• MLOps/LLMOps