Roles and Responsibilities :

- Develop Intelligent AI Solutions - Leverage state of the art AI technologies to build pioneering NLP and Generative AI solutions-such as Retrieval-Augmented Generation (RAG) pipelines and agentic workflows-that solve real-world infrastructure problems

- Own Key AI Features - Drive the end-to-end development of LLM-powered applications, chatbots, and optimization engines that improve operational efficiency and resilience.

- Build and deploy Agentic AI platforms and Applications enabling autonomous execution and orchestration. Develop AI-powered observability and autoscaling frameworks for large-scale distributed systems.

- Integrate AI/ML solutions into CI/CD pipelines, monitoring, and platform APIs.

- Collaborate with cross-functional engineers to deliver high-impact operational experience

- Serves as a subject matter expert on a wide range of ML techniques and optimizations.

- Mentor & Share Best Practices - Guide junior engineers and peers on ML design patterns, code quality, and experiment methodology.

Qualifications and Skills :

- Minimum Bachelors degrees in Data science or a related field.

- 5+ years of experience (at least 2 years working on generative AI technologies)

- Solid understanding of transformers, modern NLP / LLM techniques; experience with fine-tuning or prompting large language models.

- Strong proficiency in Python. Additionally, exposure to Golang is a plus

-Working knowledge of deployment using Kubernetes on Cloud infrastructure is desirable.

- Proven ability to build scalable, reliable production services.

- Ability to work on tasks and projects through to completion with limited supervision.

Distinguish yourself with :

- Agentic AI Mastery - Practical experience with frameworks such as LangChain or LangGraph and a deep understanding of multi-step reasoning and planning.

- LLM Inference Optimization - Expertise in accelerating LLM inference (e.g., KV caching, quantization) to achieve low latency at scale.

- End-to-End ML Systems Ownership - A portfolio showing full lifecycle ownership, from data ingestion to monitoring and continuous improvement.