Gen AI Architect

LTM; Mumbai, Pune District, Chennai District; Hybrid; INR 3,500,000 - 7,000,000; Full time

Job summary

LTM in Mumbai seeks a seasoned GenAI Architect to design and optimize advanced GenAI and ML pipelines, including LLM selection, finetuning, distillation, and governance. You will collaborate across domains to ensure regulatory compliance, performance, and reproducibility.

You will prototype MLOps/LLMOps automation, build secure, scalable architectures, and lead evaluation frameworks, RAG pipelines, and observable feature stores to enable reproducible experiments.

Qualifications

• Architect GenAI and ML pipelines with LLMs and evaluation.
• Collaborate across teams to ensure compliance, performance, reproducibility.
• Define MLOps/LLMOps automation and governance.

Responsibilities

• Select and optimize opensource LLMs for local deployment.
• Architect LLM distillation pipelines.
• Design and govern RAG pipelines for chatbots and automation.
• Build reusable AI agent frameworks.
• Architect multiagent orchestration systems.
• Design classical ML pipelines and advanced analytics models.
• Establish XAI frameworks for interpretability.
• Create LLM evaluation and quality frameworks.
• Govern prompt engineering standards.
• Automate pipelines for MLOps/LLMOps.
• Define SLAs and performance dashboards.
• Ensure security, compliance, and governance.
• Collaborate with domain, DevOps, and security teams.
• Design observability stacks.
• Architect feature stores for reproducible experiments.

Skills

Gen AI Architect

Job description

Greetings from LTM

LTM Hiring Technical AI Technical Architect !!

Mandate Skills:

Gen AI Architect

Job Summary

Responsible for architecting and optimizing advanced GenAI and classical ML pipelines, including LLM selection, finetuning, distillation, RAG pipeline design, agent framework development, evaluation frameworks, document intelligence, MLOps/LLMOps automation, security, compliance, and governance. Collaborates across teams to ensure regulatory compliance, performance, and reproducibility.

Key Responsibilities

Select and optimize opensource LLMs for local deployment and finetuning; architect LLM distillation pipelines; design and govern RAG pipelines for chatbots and automation; build reusable AI agent frameworks; architect multiagent orchestration systems; design classical ML pipelines and advanced analytics models; establish XAI frameworks for interpretability; create LLM evaluation and quality frameworks; govern prompt engineering standards; build document intelligence workflows; design semantic and hybrid search architectures; automate pipelines for MLOps/LLMOps; implement AB testing and shadow deployment; define SLAs and performance dashboards; ensure security, compliance, and governance; collaborate with domain, DevOps, and security teams; design observability stacks; architect feature stores for reproducible experiments.