Description

Key Skills: LLM Optimization, Speech-to-Text (STT), Text-to-Speech (TTS), MLOps Automation, LLM Engineering, Agentic AI, Text Analytics, Speech Recognition, Quantization, Pruning, Distillation, LoRA Fine-Tuning, CI/CD, Observability, Data Versioning, Feature Stores, GPU Job Scheduling, Cloud Deployment, AWS, System Design, System Architecture, People Management

Roles & Responsibilities:

• Lead end-to-end architecture and technical delivery for AI/ML platforms, LLM optimization initiatives, and STT/TTS services.
• Design and implement scalable, high-performance AI solutions with a focus on production-grade deployment and reliability.
• Drive model efficiency improvements using techniques such as quantization, pruning, distillation, caching, batching, and LoRA fine-tuning.
• Optimize inference latency, cost efficiency, and performance for large-scale LLM deployments and voice-based AI pipelines.
• Define and enforce engineering and MLOps standards for deployment, governance, observability, CI/CD, and model lifecycle management.
• Build and maintain robust ML automation pipelines including feature stores, retraining workflows, and GPU job scheduling.
• Productionize AI models and ensure operational readiness through automation and monitoring practices.
• Lead implementation of Agentic AI and LLM-based workflows to develop reliable AI-driven services.
• Collaborate with cross-functional teams to align AI initiatives with business objectives and product requirements.
• Mentor engineering teams, conduct technical reviews, and support knowledge-sharing initiatives.
• Contribute to system architecture and design discussions to build scalable AI platforms.
• Continuously evaluate emerging AI technologies and drive innovation within the organization.

Experience Required:

• 6 - 10 years of experience in AI/ML Engineering, Backend Engineering, or related technical domains.
• Minimum 2+ years of experience in Technical Leadership roles.
• Strong experience in LLM engineering, optimization techniques, and model evaluation.
• Hands-on expertise with Speech-to-Text (STT), Text-to-Speech (TTS), and speech recognition systems.
• Strong knowledge of MLOps automation, CI/CD pipelines, and production model lifecycle management.
• Experience with Agentic AI workflows and scalable AI service deployments.
• AWS experience for AI deployment and scaling is preferred.
• Strong system design, architecture, and people management experience is an added advantage.

Education: Any Graduation

Key Skills

Education

Any Graduate