Introduction At IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, lets talk.
Your Role and Responsibilities
Demonstrated leadership with over 12 years of experience in Data Science, specializing in machine learning, deep learning, and natural language processing. Proven ability to lead teams and take ownership of end-to-end activities.
Strong grounding in traditional AI methodologies, covering machine learning and deep learning frameworks, with the capacity to guide and mentor team members.
Proficient in utilizing model serving platforms such as TGIS and vLLM, with a knack for overseeing project implementations from conception to delivery.
Preferred expertise in transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion), coupled with hands-on involvement in testing AI algorithms and models.
Mastery of Python, C++, Go, Java, and relevant ML libraries (e.g., TensorFlow, PyTorch) for developing top-tier, production-grade products. Proven ability to lead technical teams in the implementation of complex solutions.
Skillful in full-stack development, encompassing frontend (HTML, CSS, JavaScript) and backend (Django, Flask, Spring Boot), with demonstrated experience in integrating AI technology into full-stack projects. Proficient in data integration, cleansing, and shaping, with expertise in various databases including open-source options like MongoDB, CouchDB, CockroachDB.
Capable of designing optimal data pipeline architectures for AI applications, ensuring adherence to client SLAs, while guiding team members in achieving project milestones.
Familiarity with Linux platforms and experience in Linux app development, demonstrating leadership in guiding teams through the development lifecycle.
Proficient in DevOps practices, including Git, CI/CD pipelines (Jenkins, Travis CI, GitLab CI), and containerization (Docker, Kubernetes), with a proven ability to lead teams in adopting efficient development workflows.
Experience in Generative AI is highly advantageous, with the ability to provide leadership and direction in exploring innovative AI techniques.
Proficiency in AI compiler/runtime skills, showcasing leadership in driving optimization efforts and performance enhancements.
Highly valued for open-source contribution, exhibiting leadership by actively participating in and guiding team members through contributions to open-source AI projects and frameworks.
Strong problem-solving and analytical skills, coupled with the proven ability to lead teams in optimizing AI algorithms for performance and scalability.
Adept in Agile methodologies, with a track record of leading collaborative teams in Agile development of AI-based solutions.
What you will do:
Lead the development and deployment of AI models in production environments, leveraging deep expertise in AI/ML and Data Science to ensure scalability, reliability, and efficiency.
Direct the implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, personally driving solutions for complex problems.
Personally oversee the development and deployment of large language models (LLMs) in production environments, demonstrating hands-on expertise in distributed systems, microservice architecture, and REST APIs.
Collaborate closely with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, taking a hands-on approach to ensure seamless integration and efficiency.
Proactively stay abreast of the latest advancements in AI/ML technologies and actively contribute to the development and improvement of AI frameworks and libraries, leading by example in fostering innovation.
Effectively communicate technical concepts to non-technical stakeholders, showcasing excellent communication and interpersonal skills while leading discussions and decision-making processes.
Uphold industry best practices and standards in AI engineering under your direct leadership, maintaining unwavering standards of code quality, performance, and security throughout the development lifecycle.
Demonstrate leadership in the use of container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, personally overseeing deployment strategies and optimizations.
Required Technical and Professional Expertise
Data Science and Generative AI Leadership:
Over 10 years of experience in Data Science and Generative AI, showcasing coding skills, leadership, and end-to-end ownership.
Background in machine learning, deep learning, and natural language processing, serving as a mentor and leader.
Model Development Expertise:
Hands-on experience with transformer-based and diffuser-based models (e.g., BERT, GPT, T5), mastering model development and optimization.
Desirable experience in rigorously testing AI algorithms for real-world robustness and reliability.
Traditional AI Methodologies Mastery:
Proficiency in traditional AI methodologies, including machine learning and deep learning frameworks.
Familiarity with model serving platforms like TGIS and vLLM, leading teams in model deployment.
Coding Proficiency and Product Development:
Exceptional coding proficiency in Python, C++, Go, Java, and relevant languages, focusing on production-grade products.
Extensive experience with ML libraries (TensorFlow, PyTorch), driving end-to-end AI solution development.
Full-Stack Development Leadership:
Proficient in full-stack development, frontend (HTML, CSS, JavaScript) and backend (Django, Flask, Spring Boot), integrating AI tech into projects.
Leadership in guiding teams through complex full-stack AI integration.
Data Handling and Management Excellence:
Skilled in integrating, cleansing, and shaping data, expertise in various databases (MongoDB, CouchDB, CockroachDB).
Data Pipeline Architecture Leadership:
Proficient in developing optimal data pipeline architectures, designing scalable and efficient solutions.
DevOps Leadership and Best Practices:
Experienced in DevOps practices, leading CI/CD pipelines (Jenkins, Travis CI, GitLab CI) and containerization (Docker, Kubernetes).
Open-Source Contribution and Community Leadership:
Valued contributions to open-source AI projects, driving innovation and collaboration..
Problem-Solving and Optimization Skills:
Strength in problem-solving and analytical skills, optimizing AI algorithms for performance and scalability.
AI Compiler/Runtime Skills and Innovation:
Proficiency in AI compiler/runtime skills, leveraging emerging technologies.
Agile Methodologies and Efficient Project Delivery:
– Familiarity with Agile methodologies, championing Agile development practices.
Preferred Technical and Professional Expertise
Leadership in AI/ML and Data Science:
Over 10 years of demonstrated leadership in AI/ML and Data Science, driving the development and deployment of AI models in production environments with a focus on scalability, reliability, and efficiency.
Ownership mentality, ensuring tasks are driven to completion with precision and attention to detail.
Algorithm Implementation Mastery and Optimization:
Proven track record of hands-on implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, showcasing expertise in solving complex problems effectively.
Leadership in guiding teams through algorithm implementation and optimization processes, ensuring tasks are completed with efficiency and accuracy.
Development of Large Language Models (LLMs):
Hands-on experience in the development and deployment of large language models (LLMs) in production environments, demonstrating proficiency in distributed systems, microservice architecture, and REST APIs.
Leadership in owning the end-to-end development process of LLMs, from ideation to deployment, ensuring seamless integration into production workflows.
MLOps Integration Leadership:
Experience leading cross-functional teams in the integration of MLOps pipelines with CI/CD tools for continuous integration and deployment, driving the seamless integration of AI/ML models into production workflows.
Ownership of the MLOps integration process, ensuring tasks are completed on schedule and to the highest standards of quality.
Commitment to Continuous Learning and Contribution:
Demonstrated dedication to continuous learning and staying updated with the latest advancements in AI/ML technologies.
Proven ability to contribute actively to the development and improvement of AI frameworks and libraries, showcasing leadership in driving innovation within the organization.
Effective Communication and Collaboration:
Strong communication skills, with the ability to effectively convey technical concepts to non-technical stakeholders.
Excellence in interpersonal skills, fostering collaboration and teamwork across diverse teams to drive projects to successful completion.
