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Accenture India Private Limited is seeking a Custom Software Engineer with AI Native expertise to lead end-to-end agentic AI solutions. The role focuses on designing architectures that orchestrate LLMs, RAG pipelines, and enterprise APIs to drive autonomous business workflows.
You will own multi-agent systems design, ensure security and compliance, and mentor squads while contributing to internal AI enablement and hiring efforts.
Custom Software Engineer Project Role : Custom Software Engineer Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs. Must have skills : Java Full Stack Development Good to have skills : NA Minimum 12 year(s) of experience is required Educational Qualification : 15 years full time education
Lead the design and delivery of agentic AI solutions—composable, multi agent systems that integrate LLMs, RAG pipelines, vector databases, and enterprise APIs—so teams can move from automation to autonomy across business workflows.
Strong CS and software engineering fundamentals.
Hands on experience with LLMs, RAG, vector DBs, and AI APIs.
Proven ability to build end to end applications and integrate AI into systems.
Visible portfolio of AI projects demonstrating experimentation and speed.
Fast learner with a bias for rapid prototyping and iteration.
Able to translate business problems into practical AI solutions with clear communication.
C#/.NET, Go, Rust (for performance critical agents or tooling), SQL (PostgreSQL/pgvector), Shell scripting (DevOps automation).
LangChain, LlamaIndex, Semantic Kernel vector DBs (pgvector, Pinecone, Weaviate, Milvus) messaging (Kafka/JMS) API gateways cloud SDKs (Azure/AWS/GCP).
Designing multi agent systems (planner/executor roles, collaboration protocols, tool abstraction) experience with agent frameworks or custom orchestration.
Production grade RAG (retrieval evaluators, re ranking, freshness pipelines), vector DBs, and hybrid search.
LLMOps/ModelOps: prompt/versioning, eval harnesses, feature flags, canarying, A/B testing, safety gateways.
Cloud native architecture (containers, serverless, events), secrets management, policy as code, and cost governance for AI workloads.
Enterprise integration: API gateways, event buses, messaging (JMS/Kafka), and contract testing.