LTM AI Architect F2F Drive on Oct @Bhubaneshwar No Walkin!

LTM; Khordha; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

LTM is seeking a Technical AI Architect to design and optimize GenAI and ML pipelines, including LLM selection, finetuning, distillation, and RAG workflows, with emphasis on security and governance. Collaborates across teams to ensure regulatory compliance, performance, and reproducibility.

You will collaborate with DevOps, security, and domain teams to deliver reproducible architectures, observability, and compliant AI solutions across the enterprise.

Qualifications

• Experience in GenAI architecture and ML pipelines.
• Familiarity with LLM selection, finetuning, and distillation.
• Knowledge of RAG pipelines and agent frameworks.

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.
• Establish XAI frameworks for interpretability.
• Create LLM evaluation and quality frameworks.
• Govern prompt engineering standards.

Skills

Gen AI Architect

Job description

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