Simplilearn - AI/ML Architect - GenAI/LLMs & Enterprise Automation

Job Title: AI/ML Architect - GenAI, LLMs & Enterprise Automation

Location: Bangalore

Experience: 8+ years (including 4+ years in AI/ML architecture on cloud platforms)

Role Summary

We are seeking an experienced AI/ML Architect to define and lead the design, development, and scaling of GenAI-driven solutions across our learning and enterprise platforms. This is a senior technical leadership role where you will work closely with the CTO and product leadership to architect intelligent systems powered by LLMs, RAG pipelines, and multi-agent orchestration.

You will own the AI solution architecture end-to-end-from model selection and training frameworks to infrastructure, automation, and observability. The ideal candidate will have deep expertise in GenAI systems and a strong grasp of production-grade deployment practices across the stack.

Must-Have Skills

- AI/ML solution architecture experience with production-grade systems

- Strong background in LLM fine-tuning (SFT, LoRA, PEFT) and RAG frameworks

- Experience with vector databases (FAISS, Pinecone) and embedding generation

- Proficiency in LangChain, LangGraph, LangFlow, and prompt engineering

- Deep cloud experience (AWS: Bedrock, ECS, Lambda, S3, IAM)

- Infra automation using Terraform, CI/CD via GitHub Actions or CodePipeline

- Backend API architecture using FastAPI or Node.js

- Monitoring & observability using Langfuse, LangWatch, OpenTelemetry

- Python, Bash scripting, and low-code/no-code tools (e.g., n8n)

Bonus Skills

- Hands-on with multi-agent orchestration frameworks (CrewAI, AutoGen)

- Experience integrating AI/chatbots into web, mobile, or LMS platforms

- Familiarity with enterprise security, data governance, and compliance frameworks

- Exposure to real-time analytics and event-driven architecture

You'll Be Responsible For

- Defining the AI/ML architecture strategy and roadmap

- Leading design and development of GenAI-powered products and services

- Architecting scalable, modular, and automated AI systems

- Driving experimentation with new models, APIs, and frameworks

- Ensuring robust integration between model, infra, and app layers

- Providing technical guidance and mentorship to engineering teams

- Enabling production-grade performance, monitoring, and governance