LLMOps Lead / AI Platform Architect

Technozis; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Technozis is seeking an LLMOps / AI Platform Architect to design, build, and evolve enterprise-scale AI systems. The role emphasizes hands-on architecture, scalable AI platform components, and production-ready pipelines.

You will lead RAG pipelines, multi-agent workflows, and governance while collaborating with product and data teams to deliver secure, observable, and high-performance AI solutions.

Qualifications

• 9–15 years of AI/ML experience with hands-on delivery.
• Deep hands-on with LLMs and Generative AI.
• Production-grade AI systems design and deployment.
• Experience with RAG pipelines and multi-agent workflows.
• Proficient in Python and cloud platforms (AWS/Azure/GCP).
• Docker, Kubernetes, and ML observability practices.

Responsibilities

• Architect end-to-end LLM-based systems with RAG and agents.
• Define scalable patterns for low latency and high throughput.
• Lead model selection, vector DB use, and AI orchestration.
• Build reusable AI platform components for enterprise use.
• Establish CI/CD for ML/LLM pipelines and governance.
• Drive evaluation, benchmarking, and prompt engineering strategies.
• Mentor AI/ML engineers and promote engineering excellence.
• Collaborate with product and data teams on solutions.

Skills

LLMOps

Generative AI

Python

RAG pipelines

Multi-agent systems

Docker

Kubernetes

Cloud platforms

LLM observability

Architecture leadership

Tools

FAISS

Pinecone

Milvus

LangChain/LangGraph/AutoGen

Job description

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

This is a hands-on architecture and platform engineering role, focused on building innovative, scalable, secure, and production-ready AI systems.

The role involves architecting next-generation AI capabilities including RAG systems, Agentic AI workflows, enterprise copilots, and decision intelligence platforms.

*Note: This is not a pure LLM operations or support role. Strong hands-on engineering and architecture experience is required.

Key Responsibilities

• Architect end-to-end LLM-based systems including RAG pipelines, multi-agent workflows, LLM adaptation, and enterprise AI solutions.
• Define architecture patterns for scalability, low-latency inference, and high-throughput workloads.
• Lead technical decisions around model selection, retrieval strategies, vector databases, hybrid search, and AI orchestration frameworks.
• Design scalable and reusable AI platform components.
• Design and build enterprise-grade LLM platforms supporting rapid experimentation and standardized deployment.
• Establish best practices for CI/CD across ML and LLM systems.
• Define model and version lifecycle management practices.
• Build evaluation and benchmarking frameworks for LLM applications.
• Enable scalable deployment using cloud-native and Kubernetes-based architectures.
• Lead development of enterprise copilots, automation agents, and decision intelligence solutions.
• Develop advanced prompt engineering and agent orchestration strategies.
• Design LLM evaluation and feedback mechanisms.
• Research and integrate emerging Generative AI tools, frameworks, and technologies.

Leadership & Team Building

• Build and mentor teams of AI Engineers, ML Engineers, and Platform Engineers.
• Provide technical direction and code-level guidance.
• Promote engineering excellence, innovation, and ownership.

Responsible AI, Security & Governance

• Design guardrails for hallucination control and prompt injection prevention.
• Implement PII and data protection mechanisms.
• Establish frameworks for model evaluation, explainability, auditability, and enterprise AI governance.

Cross-Functional Collaboration

• Partner with product, business, data, and platform teams.
• Translate business requirements into scalable AI solutions.
• Support solution design and pre-sales activities for AI initiatives

.

Mandatory Skills & Qualifications

• 9–15 years of overall AI/ML experience.
• Strong hands-on experience with LLMs and Generative AI.
• Proven experience designing and deploying production-grade AI systems.
• Strong hands-on experience with: RAG pipelines; Agentic AI / multi-agent systems; Vector databases such as FAISS, Pinecone, or Milvus; LangChain / LangGraph / AutoGen or similar frameworks
• Strong Python programming and coding skills — mandatory.
• Experience with AWS, Azure, or GCP.
• Strong understanding of LLMOps / MLOps practices.
• Experience with Docker and Kubernetes.
• Strong understanding of scalable AI system architecture and platform engineering.
• Strong understanding of prompt engineering and LLM observability.

Preferred Qualifications

• Experience building multi-agent systems or enterprise AI copilots.
• Experience with LLM evaluation frameworks such as RAGAS or LangSmith.
• Experience with LLM fine-tuning, LoRA, or PEFT.
• Exposure to enterprise AI governance and compliance.
• Experience leading AI/ML engineering teams or architecting enterprise AI platforms.

What Makes This Role Unique

• Opportunity to architect enterprise-scale AI platforms.
• Work on cutting-edge areas including Agentic AI, enterprise copilots, RAG, and decision intelligence.
• High ownership and visibility across AI initiatives.
• Strong combination of architecture, innovation, and hands-on engineering.