Job responsibilities:
•Establish and promote a library of common ML assets, including reusable ML models, features stores, data pipelines, and standardized templates.
•Lead efforts to create shared tools and platforms that streamline the end-to-end ML lifecycle across the organization.
•Create curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.
•Gain Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines
•Advise on the strategy and development of multiple products, applications, and technologies.
•Lead advisor on the technical feasibility and business need for AIML use cases.
•Liaise with firm wide AI ML stakeholders.
•Translate highly complex technical issues, trends, and approaches to leadership to drive the firm's innovation and enable leaders to make strategic, well informed decisions about technology advancements.
•Influence across business, product and technology teams and successfully manages senior stakeholder relationships.
•champion the firm's culture of diversity, opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills:
•Formal training or certification on Machine Learning concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
•MS and/or PhD in Computer Science, Machine Learning, or a related field
•At least 10 years’ experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
•Solid understanding of using ML techniques specially in Natural Language Processing (NLP) and Large Language Models (LLMs)
•Hands-on experience with machine learning and deep learning methods.
•Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team.
•Good understanding 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, RAG (Similarity Search).
•Ability to work on system design from ideation through 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.
•Practical cloud native experience such as AWS needed.
Preferred qualifications, capabilities, and skills:
•Experience with Ray, MLFlow, and/or other distributed training frameworks.
•In-depth understanding of Embedding based 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.