Sr. Software Architect-AI Job at Ericsson (Noida)

Job Description

Join Ericsson as a Senior Software Architect – AI, owning end-to-end architecture for enterprise-scale GenAI and AI-powered solutions within our Self-Service Platform (SSP). You will design scalable, secure, production-grade AI platforms leveraging agentic frameworks, LLMs, and cloud-native services. You will establish reference architectures across RAG, memory, evaluation, and observability while guiding teams across the full model lifecycle — at the intersection of AI innovation and Responsible AI governance.

Job Responsibility

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)

Requirements

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
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
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

Nice to have

LLM observability tools (Arize, LangSmith)
Azure enterprise services (AKS, Key Vault)
memory frameworks (MemGPT, LangMem)
knowledge graph experience
Telecom industry AI adoption background

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Sr. Software Architect-AI

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