Summary: Develops applications and systems that utilize AI to improve performance and efficiency, including but not limited to deep learning, neural networks, chatbots, natural language processing.
Responsibilities:
- Formulate real-world problems into practical, efficient, and scalable AI and Machine Learning solutions
- Develop and implement machine learning algorithms, models and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps
- Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements including edge device and HPC
- Engage in research and development of new AI and high-performance compute algorithms, models, and simulations along with their applications to solve complex problems
- Work with large-scale datasets and utilize data preprocessing techniques to ensure high-quality input for training and production
- Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools
- Justify the value of model approaches in the business problems
- Collaborate with teams from both business and technical sides, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project goals and integrate into production
- Design and develop artificial intelligence (AI) and machine learning (ML) systems, leveraging existing cloud AI services as appropriate.
- Design and build scalable data pipelines to support model training and production with DevOps & MLOps.
- Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements including edge device and High Performance Computer (HPC).
- Justify the value of model approaches in the business problems.
- Engage in research and development of new AI and high-performance compute algorithms, models, and simulations along with their applications to solve complex problems.
- Work with large-scale datasets and utilize data preprocessing techniques to ensure high-quality input for training and production.
- Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools.
Job Qualifications
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related field. MBA is a plus.
- Minimum of 10 years of experience in data, large language models, machine learning, and AI, with a proven track record of leading and managing data teams in a consulting environment.
- Proficiency in programming languages such as Python, R, or Java.
- Strong expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and data manipulation libraries (e.g., pandas, NumPy).
- Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP
- Advanced analytical and problem-solving skills, with the ability to analyze complex datasets and derive meaningful insights.
- Strong understanding of statistical methods and their application in data analysis and modeling.
- Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders effectively.
- Proven leadership and team management skills, with the ability to inspire and motivate a high-performing team.
- Demonstrated ability to understand client needs, develop tailored data science solutions, and build long-lasting client relationships.
- Ability to adapt to rapidly changing technologies and methodologies in the data science and consulting domains.
- Certifications (Optional, but a Plus):Relevant certifications in data science, machine learning, or project management (e.g., PMP, AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer etc).
