Serve as a subject matter expert on a wide range of ML techniques and optimizations.
Provide in-depth knowledge of ML algorithms, frameworks, and techniques.
Enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
Conducting experiments using the latest ML technologies, analyzing results, tuning models
Hands-on coding to bring the experimental results into production solutions by collaborating with the engineering team. Owning end-to-end code development in Python for both proof of concept/experimentation and production-ready solutions.
Optimizing 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.
Integrate Generative AI within the ML Platform using state-of-the-art techniques.
Drives decisions that influence the product design, application functionality, and technical operations and processes.
Requirements & Skills:
MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience.
At least 5 years experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
At least 5 years’ experience in applying data science, ML techniques to solve business problems.
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, finetuning, embedding models, inferencing, prompt engineering, evaluation, and 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
Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
Experience with Ray, MLFlow, and/or other distributed training frameworks.
In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
Advanced knowledge in Reinforcement Learning or Meta-Learning.
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.
