Key Responsibilities

• Design and implement Applied AI solutions for real-world engineering and system problems.

• Build agentic AI systems that can reason, plan, and interact with tools and data.

• Develop PoCs to validate AI use cases and rapidly transition successful PoCs into usable tools.

• Create AI-powered tools to improve productivity of engineering and product teams.

• Design and implement RAG (Retrieval-Augmented Generation) systems using structured and unstructured data.

• Build feedback and evaluation systems to continuously improve AI performance.

Required Skills & Qualifications

•  Strong experience in Python and/or PyTorch.

•  Solid understanding of conventional Machine Learning and Deep Learning.

•  Strong knowledge of NLP techniques, transformers, embeddings, and language models.

•  Hands-on experience with CNNs and LSTMs.

•  Experience with fine-tuning models (LLMs or task-specific models).

•  Experience building and operating RAG systems.

•  Ability to design end-to-end AI pipelines, from data ingestion to inference.