LLMOps (LLM Platform Engineering) Lead / AI Platform Architect

Python coding is must.

Role Overview

We are looking for a Lead – LLMOps / AI Platform Architect to drive the design, development, and evolution of enterprise-scale LLM-powered systems and AI platforms.

This role goes beyond traditional MLOps — you will architect next-generation AI capabilities, including RAG systems, agentic workflows, enterprise copilots, and decision intelligence platforms, while ensuring they are scalable, secure, and production-ready.

NOTE: This is a hands-on architecture and platform leadership role, focused on building innovative AI systems — not a pure operations/support function.

Key Responsibilities

AI Architecture & Platform Design

Architect end-to-end LLM-based systems, including:

Retrieval-Augmented Generation (RAG)

Multi-agent / agentic workflows

LLM fine-tuning and adaptation strategies

Define system design patterns for:

scalability low-latency inference high-throughput workloads

Lead architectural decisions across

model selection (open-source vs API-based)

retrieval strategies (vector DB, hybrid search)

orchestration frameworks (LangGraph, LangChain, etc.)

LLM Platform Engineering

Design and build enterprise-grade LLM platforms enabling:

rapid experimentation standardized deployment reusable AI components

Establish best practices for

CI/CD for ML & LLM systems model/version lifecycle management evaluation and benchmarking frameworks

Enable scalable deployment using cloud-native and Kubernetes-based architectures

AI Innovation & Use Case Development

Lead development of

enterprise copilots decision intelligence systems automation agents

Drive innovation in

prompt engineering strategies agent orchestration patterns

LLM evaluation and feedback loops

Explore and integrate emerging tools, frameworks, and research in generative AI





Leadership & Team Building

Build and mentor a high-performing team of:

AI engineers

ML engineers platform engineers

Provide technical direction and code-level guidance

Foster a culture of

innovation ownership engineering excellence

Responsible AI, Security & Governance

Define and implement guardrails for:

hallucination control prompt injection prevention

PII/data protection

Ensure compliance with enterprise-grade security standards

Establish governance frameworks for

model evaluation explainability auditability

Cross-Functional Collaboration

Partner with

product teams business stakeholders data/platform teams

Translate business problems into scalable AI solutions

Support pre-sales and solution design for AI-driven initiatives

Required Qualifications

8+ years of experience in AI/ML, with strong exposure to LLMs / Generative AI

Proven experience designing and deploying production-grade AI systems

Hands-on experience with:

RAG pipelines vector databases (FAISS, Pinecone, Milvus, etc.)

agentic frameworks (LangChain, LangGraph, AutoGen, etc.)

Strong programming skills in Python

Experience with cloud platforms (AWS / Azure / GCP)

Deep understanding of

MLOps / LLMOps practices containerization (Docker, Kubernetes)

scalable system design

Preferred Qualifications

Experience with multi-agent systems or AI copilots

Familiarity with LLM evaluation frameworks (RAGAS, LangSmith, etc.)

Experience with fine-tuning / LoRA / PEFT

Exposure to enterprise AI governance and compliance

Prior experience in leading teams or architecting platforms

What Makes This Role Unique

Prospect to architect enterprise-scale AI platforms from the ground up

Work on cutting-edge areas like:

agentic AI decision intelligence enterprise copilots

High ownership and visibility across the organization

Strong balance of

innovation architecture engineering excellence

📌 LLMops Architect (Bengaluru)
🏢 Technozis
📍 Bengaluru