JOB DESCRIPTION

In this role you will work closely with our strategic customers, both internal and external, helping them develop end-to-end solutions and apply Generative AI, as well as other ML models to their business scenarios. You will partner with our outbound and inbound product management, engineering, data science, research, and sales teams to help customers be successful using our products and influence our roadmap. 

Core Responsibilities

• Develop, implement, and optimize large language models and Generative AI technologies, including training/finetuning and computation optimizations.
• Collaborate with software engineers to deploy LLM and other Generative AI models into production environments.
• Stay up to date with the latest advancements in the field of Generative AI.
• Collaborate with cross-functional teams to drive the development and adoption of LLM and Generative AI solutions across various organizations in the company.
• Work directly with key customers and accompany them on their AI journey – understanding their requirements, help them envision and design the right solutions and work with their engineering and data science team to remove blockers and translate feedback into actionable items for individual service owners.
• Design and build solutions to help customers reach successful pilots, PoCs and production deployments with our Generative AI and Data Science technologies. 
• Consolidate learnings from these strategic engagements and present to the inbound product management teams to improve their products and platforms.
• Build re-usable solution patterns and reference solutions / showcases that can apply across multiple customers 
• Be an enthusiastic, self-motivated, and great collaborator with a can-do attitude 
• Be a product evangelist - engage directly with customers and partners, participate and present in external events and conferences, when required 

Qualifications:

• Advanced degree in computer science, engineering, or a field related to deep learning and/or NLP. 
• Strong knowledge of ML fundamentals – supervised vs unsupervised modeling, time series, highly unbalanced and noisy data sets, complex feature engineering, recommendation systems, using and optimizing gradient boosting models, NLP, deep learning on all kinds of unstructured data.
• 5+ (for Senior), 7+ (for Principal), 10+ (for Sr Principal) years of work experience including a minimum of 2-year experience in developing large-scale ML solutions, and in particular deep learning solutions in the NLP field.
• Proficiency with deep learning frameworks such as PyTorch or TensorFlow, and deep learning architectures, especially Transformers.
• Hands-on experience with distributed training of large language models.
• Strong development experience of deep learning modeling in Python.
• Familiarity with the latest advancements in LLM and Generative AI technologies.
• Familiarity with engineering best practices, including shared codebase, version control, containerization, etc.
• Passionate about being a builder and working with talented peers to solve hard problems at scale.
• Good communication skills to convey technical concepts in straightforward terms with product managers and various stakeholders.

Preferred Skills

• Publications in top-tier deep learning conferences or significant contributions to prominent deep learning repositories
• Industrial experience in system design, software development, and production deployment
• Excel in transforming ambiguous requirements into actionable plans with deep learning techniques for problem-solving.
• First-hand experience with deep reinforcement learning
• First-hand experience with the latest technologies in LLM and Generative AI such as parameter-efficient finetuning and instruction finetuning 
• Familiarity with the latest advancements in computer vision and multimodal modeling such as CLIP, DALL-E2, and diffusion models
• Top-tier performance in prestigious deep learning leaderboards or large model-related competitions. 
• Having an impressive set of GitHub projects or contributions to open-source technologies

Additional Responsibilities

Defines the bar for science practices, and helps teams achieve those goals.  Identifies and mitigates risks across full set of systems, particularly at the intersection of business and engineering. Innovate AI and ML powered solutions (rich APIs, ML models and end to end services) with strategic ISVs and customers. Develop deep product intuition to influence future product roadmaps and drive decision making. Clearly articulate technical work to audiences of all levels and across multiple functional areas in both internal and external settings. Engage in forward looking research both internal and with academic institutions globally. Invent, implement, and deploy state-of-the-art machine learning and/or specific domain industry algorithms and systems. Build prototypes and explore conceptually new solutions. Work collaboratively with science, engineering, and product teams to identify customer needs to create and implement solutions, promote innovation, and drive model implementations.  Applies data science capabilities and research findings to create and implement solutions to scale. Responsible for developing new intelligence around core products and services through applied research on behalf of customers.  Develops models, prototypes, and experiments that pave the way for innovative products and services.  Build cloud services that work out of the box for enterprises, e.g., decision support, anomaly detection, forecasting and recommendations, natural language processing (NLP), Natural Language Understanding (NLU), Time Series, Automatic Speech Recognition (ASR), Machine Learning (ML), and Computer Vision (CV). Design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. Conversant on ethical problems in consideration of sciences.

Responsibilities

Core Responsibilities

• Develop, implement, and optimize large language models and Generative AI technologies, including training/finetuning and computation optimizations.
• Collaborate with software engineers to deploy LLM and other Generative AI models into production environments.
• Stay up to date with the latest advancements in the field of Generative AI.
• Collaborate with cross-functional teams to drive the development and adoption of LLM and Generative AI solutions across various organizations in the company.
• Work directly with key customers and accompany them on their AI journey – understanding their requirements, help them envision and design the right solutions and work with their engineering and data science team to remove blockers and translate feedback into actionable items for individual service owners.
• Design and build solutions to help customers reach successful pilots, PoCs and production deployments with our Generative AI and Data Science technologies. 
• Consolidate learnings from these strategic engagements and present to the inbound product management teams to improve their products and platforms.
• Build re-usable solution patterns and reference solutions / showcases that can apply across multiple customers 
• Be an enthusiastic, self-motivated, and great collaborator with a can-do attitude 
• Be a product evangelist - engage directly with customers and partners, participate and present in external events and conferences, when required 

Additional Responsibilities

Defines the bar for science practices, and helps teams achieve those goals.  Identifies and mitigates risks across full set of systems, particularly at the intersection of business and engineering. Innovate AI and ML powered solutions (rich APIs, ML models and end to end services) with strategic ISVs and customers. Develop deep product intuition to influence future product roadmaps and drive decision making. Clearly articulate technical work to audiences of all levels and across multiple functional areas in both internal and external settings. Engage in forward looking research both internal and with academic institutions globally. Invent, implement, and deploy state-of-the-art machine learning and/or specific domain industry algorithms and systems. Build prototypes and explore conceptually new solutions. Work collaboratively with science, engineering, and product teams to identify customer needs to create and implement solutions, promote innovation, and drive model implementations.  Applies data science capabilities and research findings to create and implement solutions to scale. Responsible for developing new intelligence around core products and services through applied research on behalf of customers.  Develops models, prototypes, and experiments that pave the way for innovative products and services.  Build cloud services that work out of the box for enterprises, e.g., decision support, anomaly detection, forecasting and recommendations, natural language processing (NLP), Natural Language Understanding (NLU), Time Series, Automatic Speech Recognition (ASR), Machine Learning (ML), and Computer Vision (CV). Design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. Conversant on ethical problems in consideration of sciences.