Roles and Responsibilities:
• Lead the design, development, and implementation of AI/ML-based solutions across various product lines.
• Collaborate with product managers, data engineers, and architects to translate business requirements into data science problems and solutions.
• Take ownership of end-to-end AI/ML modules, from data processing to model development, testing, and deployment.
• Provide technical leadership to a team of data scientists, ensuring high-quality outputs and adherence to best practices.
• Conduct cutting-edge research and capability building across the latest Machine Learning, Deep Learning, and AI technologies.
• Prepare technical documentation, including high-level and low-level design, requirement specifications, and white papers.
• Evaluate and fine-tune models, ensuring they meet performance requirements and deliver insights that drive product improvements.
• Production exposure to Large Language Models (LLM) and experience in implementing and optimizing LLM-based solutions.
Must-have Skills:
• 4-6 years of experience in Data Science and AI/ML product development, with a proven track record of leading technical teams.
• Expertise in machine learning algorithms, Deep Learning models, Natural Language Processing, and Anomaly Detection.
• Strong understanding of model lifecycle management, including model building, evaluation, and optimization.
• Hands-on experience with Python and proficiency with frameworks like TensorFlow, Keras, PyTorch, etc.
• Solid understanding of SQL, NoSQL databases, and data modeling with ElasticSearch experience.
• Ability to manage multiple projects simultaneously in a fast-paced, agile environment.
• Excellent problem-solving skills and communication abilities, particularly in documenting and presenting technical concepts.
• Familiarity with Big Data frameworks such as Spark, Storm, Databricks, and Kafka.
• Experience with container technologies like Docker and orchestration tools like Kubernetes, ECS, or EKS
Optional (Good To Have) Skills:
• Experience with cloud-based machine learning platforms like AWS, Azure, or Google Cloud.
• Experience with tools like MLFlow, KubeFlow, or similar for model tracking and orchestration.
• Exposure to NoSQL databases such as MongoDB, Cassandra, Redis, and Cosmos DB, and familiarity with indexing mechanisms.
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