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