Sr. Software Architect-AI

• Architect agentic AI applications using LangChain, LangGraph, and orchestration patterns; define prompt strategies, guardrails, and structured outputs aligned to product and risk requirements.
• Design and optimize RAG solutions (chunking, embeddings, retrieval, re-ranking) and own foundation model integrations (Azure OpenAI, AWS Bedrock, on-prem LLMs) with routing, fallbacks, and cost/performance optimization.
• Define GenAI reference architectures; evaluate and select LLMs, embedding models, vector databases, and orchestration frameworks based on performance, compliance, and cost.
• Embed security, privacy, and Responsible AI governance from inception — covering PII handling, data access controls, and content guardrails.
• Build scalable backend APIs using Python (FastAPI, asyncio) with REST/JSON-RPC interfaces and resilience patterns (Redis, RabbitMQ); guide teams on MLOps/LLMOps standards including deployment, monitoring, retraining, and drift handling.
• Define LLM evaluation strategies, implement observability/tracing (Arize, LangSmith), and design memory strategies with retention and replay safety for long-running assistants.
• Containerize and deploy services via Docker and Kubernetes; govern CI/CD pipelines with automated testing, security scanning, and IaC (Terraform or equivalent).

The skills you bring:

• BE/B.Tech/MCA in Computer Science, Engineering, or equivalent, with 15+ years in software architecture and relevant 3+ years  designing AI/ML or LLM-based systems in production.
• All academic credentials must be from recognized and accredited institutions and are further subject to verification.” 
• Deep expertise in Python (FastAPI, asyncio) and ML/DL frameworks (PyTorch, TensorFlow); strong experience with distributed, cloud-native services.
• Hands-on with RAG pipelines, embeddings, and vector databases (Elastic, Pinecone, Milvus, Chroma) for enterprise knowledge grounding.
• Hands on Python experience mandatory
• Proven experience with agentic GenAI frameworks (LangChain, LangGraph, LlamaIndex, AutoGen) and interoperability patterns such as Model Context Protocol (MCP).
• Strong knowledge of LLM architectures, fine-tuning techniques (LoRA, PEFT), and experience with Azure OpenAI and/or AWS Bedrock.
• Solid understanding of MLOps/LLMOps, Responsible AI principles, and embedding governance into GenAI design.
  Proficiency with Docker, Kubernetes, Terraform, and CI/CD for cloud-native AI deployments.
• Good to Have: LLM observability tools (Arize, LangSmith), Azure enterprise services (AKS, Key Vault), memory frameworks (MemGPT, LangMem), knowledge graph experience, and Telecom industry AI adoption background.
• Locations: Bangalore, Kolkata, Gurgaon, Noida, Chennai
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