Applied AI ML Associate

Bring your Applied Research and AIML skills to the next level and bring your advanced expertise in Machine Learning (ML) to join our dynamic AIML Data Platforms Team. As an Applied AI ML Associate within our Corporate Sector, you will play a pivotal role in developing machine learning and deep learning solutions and experimenting with state-of-the-art models. Join our dynamic team dedicated to building a high-impact solution using generative technologies. We are focused on developing a multi-agent central platform that will be utilized by various Lines of Business (LOBs). Our team actively collaborates with customers to develop, optimize, and support business-critical use cases, ensuring that our platform meets the evolving needs of our clients.

Serves as a subject matter expert on a wide range of ML techniques and optimizations. Provides in-depth knowledge of ML algorithms, frameworks, and techniques. Enhances ML workflows through advanced proficiency in large language models (LLMs) and related techniques. Conducts experiments using latest ML technologies, analyzing results, tuning models Provides has Hands on coding to bring the experimental results into production solutions by collaborating with engineering team. Owning end to end code development in python for both proof of concept/experimentation and production-ready solutions. Optimizes system accuracy and performance by identifying and resolving inefficiencies and bottlenecks. Collaborates with product and engineering teams to deliver tailored, science and technology-driven solutions. Integrates Generative AI within the ML Platform using state-of-the-art techniques. Required qualifications, capabilities, and skills Experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must. Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs) Hands-on experience with machine learning and deep learning methods. Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow. Experience in advanced applied ML areas such as GPU optimization, fine tuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search). Ability to work on tasks and projects through to completion with limited supervision. Passion for detail and follow through. Excellent communication skills and team player. Preferred qualifications, capabilities, and skills Master’s degree in computer science, ML or related areas Experience with Ray, MLFlow, and/or other distributed training frameworks. In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services.