
Simplilearn - AI/ML Architect - GenAI/LLMs & Enterprise Automation
3 weeks ago
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
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