AI Architect

Ericsson; Kolkata District; Hybrid; INR 3,000,000 - 7,000,000; Full time

Job summary

Ericsson is seeking a Senior Software Architect AI to own end-to-end architecture for enterprise-scale GenAI and AI-powered solutions within our Self-Service Platform. You will design scalable, secure, production-grade AI platforms leveraging LLMs and cloud-native services.

You will guide teams across model lifecycles, implement governance, and optimize RAG pipelines, embeddings, and memory strategies while ensuring cost efficiency and robust observability.

Qualifications

• 15+ years in software architecture with AI/ML or LLM-based systems in production.
• All academic credentials must be from recognized institutions and subject to verification.
• Hands-on Python experience is mandatory.

Responsibilities

• Architect agentic AI applications using LangChain, LangGraph, and orchestration patterns.
• Design and optimize RAG solutions with embeddings, retrieval, and model integrations (Azure OpenAI, AWS Bedrock, on-prem LLMs).
• Define GenAI reference architectures; evaluate and select LLMs, embeddings, vector stores, and orchestration frameworks.
• Embed security, privacy, and Responsible AI governance from inception, including data access controls and guardrails.
• Build scalable backend APIs using Python (FastAPI) with REST/JSON-RPC and resilience (Redis, RabbitMQ); guide MLOps/LLMOps standards.
• Define LLM evaluation strategies; implement observability/tracing and memory strategies for long-running assistants.
• Containerize and deploy with Docker/Kubernetes; govern CI/CD with IaC (Terraform) and security scans.

Skills

Python programming

AI/ML architecture

GenAI platforms

LLM integration

MLOps/LLMOps

Education

BE/B.Tech in Computer Science

MCA

Tools

Docker

Kubernetes

Terraform

FastAPI

LangChain

LangGraph

Azure OpenAI

AWS Bedrock

Pinecone

Milvus

Elastic

Chroma

Redis

RabbitMQ

Arize

LangSmith

Job description

About this opportunity:

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.

What you will do:

• 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