Chennai, Tamil Nadu
Job Summary
10+ years of enterprise software engineering experience with bachelor’s or master’s degree in computer science, Software Engineering, or a related technical discipline
Experience in solution architecture and SDLC transformation initiatives

Generative AI & Agent Technologies (2+ Years)

Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.
Robust knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI

.NET Technologies

Extensive hands-on experience with C# (.NET 6/7/8), ASP.NET Core, and the broader .NET ecosystem
Microservices and API development using ASP.NET Core Web API, gRPC, and Minimal APIs; experience with Semantic Kernel for .NET AI integration
Messaging and event-driven architecture: Azure Service Bus, Apache Kafka, RabbitMQ, or AWS SQS/SNS
Persistence: Entity Framework Core, Dapper, SQL Server, PostgreSQL, Azure Cosmos DB, Redis
DevOps tooling: Docker, Kubernetes, Azure DevOps pipelines, GitHub Actions, NuGet package management

Solution Architecture & SDLC Transformation

Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices

Domain Experience

Experience in the life insurance domain: underwriting, claims processing, policy administration,



or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)
Key Responsibilities

Soft Skills & Professional Competencies

Excellent written communication skills — ability to produce clear technical documents, architecture proposals, and executive summaries tailored to both technical and non-technical audiences
Strong verbal communication — able to articulate complex AI/engineering concepts confidently in meetings, workshops, and presentations
Stakeholder engagement — proven ability to work closely with business leaders, product owners, and cross-functional teams to align technical solutions with business goals
Collaborative team player — comfortable working in distributed, agile teams while also being self-driven as an individual contributor
Mentoring & knowledge sharing — willingness to coach junior engineers and contribute to a culture of continuous learning
Analytical thinking — structured problem-solver who can break down ambiguous challenges and propose pragmatic, scalable solutions
Adaptability — thrives in fast-moving environments where AI technologies and business priorities evolve rapidly
Skill Requirements

Generative AI & Agent Technologies (2+ Years)

Hands-on experience building GenAI Agent systems using LangChain, LangGraph, CrewAI, AutoGen, MCP Protocol.




Strong knowledge of LLM APIs: Anthropic Claude, OpenAI GPT-4o
Experience implementing Retrieval-Augmented Generation (RAG) pipelines and vector stores (Pinecone, Weaviate, pgvector, Chroma)
Proficiency in prompt engineering techniques: Chain-of-Thought, ReAct, few-shot/zero-shot strategies
Exposure to cloud AI platforms: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI

.NET Technologies

Extensive hands-on experience with C# (.NET 6/7/8), ASP.NET Core, and the broader .NET ecosystem
Microservices and API development using ASP.NET Core Web API, gRPC, and Minimal APIs; experience with Semantic Kernel for .NET AI integration
Messaging and event-driven architecture: Azure Service Bus, Apache Kafka, RabbitMQ, or AWS SQS/SNS
Persistence: Entity Framework Core, Dapper, SQL Server, PostgreSQL, Azure Cosmos DB, Redis
DevOps tooling: Docker, Kubernetes, Azure DevOps pipelines, GitHub Actions, NuGet package management

Solution Architecture & SDLC Transformation

Demonstrated experience leading architecture reviews, technical road-mapping, and design-pattern governance
Ability to transform traditional SDLC processes with AI-assisted development, automated testing, and DevSecOps practices

Domain Experience

Experience in the life insurance domain: underwriting, claims processing, policy administration, or actuarial tooling
Knowledge of insurance regulatory and data-governance standards (SOX, GDPR equivalents)

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