Responsibilities :

A day in the life of an Infoscion

As part of the Infosys delivery team, your primary role would be to interface with the client for quality assurance, issue resolution and ensuring high customer satisfaction.

You will understand requirements, create and review designs, validate the architecture and ensure high levels of service offerings to clients in the technology domain.

You will participate in project estimation, provide inputs for solution delivery, conduct technical risk planning, perform code reviews and unit test plan reviews.

You will lead and guide your teams towards developing optimized high quality code deliverables, continual knowledge management and adherence to the organizational guidelines and processes.

You would be a key contributor to building efficient programs/ systems and if you think you fit right in to help our clients navigate their next in their digital transformation journey, this is the place for you! If you think you fit right in to help our clients navigate their next in their digital transformation journey, this is the place for you!

Additional Responsibilities:

Preferred Skills Azure OpenAI Services AWS Bedrock Databricks Mosaic AI AI Observability tools (LangSmith, PromptFlow, Phoenix) Docker & Kubernetes MLOps / LLMOps GraphRAG Knowledge Graphs

Technical and Professional Requirements:

5-9 years of software development experience Strong hands-on experience in Generative AI Experience with Agentic AI architectures Expertise in AI Agent Frameworks such as: LangGraph AutoGen CrewAI Semantic Kernel LangChain Agents Strong experience in RAG (Retrieval-Augmented Generation) Hands-on experience with MCP (Model Context Protocol) Experience working with LLMs (OpenAI, Azure OpenAI, Claude, Gemini, Llama) Python development experience Prompt Engineering and AI application development Vector Databases: Pinecone, ChromaDB, Weaviate, FAISS, Azure AI Search Roles & Responsibilities Design and build enterprise-grade Agentic AI applications. Develop multi-agent workflows using modern agent frameworks. Implement RAG pipelines leveraging vector databases and enterprise knowledge sources. Integrate AI agents with enterprise applications using MCP. Optimize prompts, retrieval strategies, and agent orchestration workflows. Develop APIs and backend services supporting GenAI solutions. Deploy, monitor, and fine-tune LLM-powered applications. Collaborate with business and engineering teams to identify AI use cases and solution strategies. Ensure scalability, security, and responsible AI practices.

Gen AI Consultant