As a software engineer you will be closely working with engineering team on scaling Agentic Ai capabilities.

8+ years of working experience in web-development or solutions architecture, with a proven track record of developing consumer-facing and internal solutions.

Experience implementing large-scale technological enhancements and/or pivots, including pilot implementation and analysis.

Strong experience, on Generative Ai, Agentic AI, LLM, RAG, Langhchain, Langhgraph

Strong System Design skills

Excellent understanding of DevOps; chef and puppet technique.

Understanding of multiple programming languages, including at least framework

Experience working with IT infrastructure and cloud development.

Experience working with data and DBMS, including legacy and emerging database technologies.

Experience with RDBMS, NoSQL.

Experience building highly available customer-facing applications, in a GDHA setting.

Experience building cloud-native applications.

Experience with API's.

Experience designing highly available and resilient solutions; ability to identify performance improvement opportunities and transform traditional monolith architecture to contemporary microservices-based loosely decoupled architecture.





A bachelor's degree or foreign equivalent in computer science or a related field.

Experience in 2 or 3 of the following technology areas: Infrastructure, Security, DevOps, Application Development, Database technologies, Cloud computing (AWS, Azure, GCP).

AI Platform & Technical Skills

Candidates should demonstrate knowledge and hands-on experience across the following enterprise AI architecture competencies: AI Platform & Model Access

Designing model consumption patterns (API, AI gateway, vendor managed AI).

Understanding centralized vs embedded model access.

Azure OpenAI, AWS Bedrock, provider-embedded AI (Salesforce, ServiceNow, Microsoft co-pilot studio). AI Platform Architecture & MLOps

Enterprise RAG architecture patterns; embedding lifecycle management.

Agentic workflows, vector stores and enterprise capabilities integration.

AWS SageMaker, Amazon Bedrock Agentcore and similar platforms.

MLOps/LLMOps (MLFlow): prompt versioning, model registry, model gateway across lifecycle stages. Agent & Orchestration Frameworks

Agent vs workflow vs rules-based decisioning; single-agent vs multi-agent orchestration.

Copilot Studio, Salesforce Agentforce, conceptual LangChain/LangGraph literacy.

📌 Senior AI Engineer (Bengaluru)
🏢 Infosys
📍 Bengaluru