Lead AI Engineer

TheMathCompany or MathCo® is a global Enterprise AI and Analytics company trusted for data-driven decision-making by leading Fortune 500 and Global 2000 enterprises. Founded in 2016, MathCo builds custom AI and advanced analytics solutions focused on enterprise problem-solving through its innovative hybrid model. NucliOS, MathCo’s proprietary platform with pre-built workflows and reusable plug-and-play modules, enables the vision of connected intelligence at a lower TCO. For our employees, we foster an open, transparent, and collaborative culture with no barriers, making MathCo a great place to work. We provide exciting growth opportunities, value capabilities, and attitude over experience, enabling the Mathemagicians to ‘Leave a Mark’. Responsibilities • Responsible for leading a team of talented AI Analysts or Engineers responsible for designing, building, and maintaining scalable AI solutions • Develop and implement advanced Generative AI solutions (LLMs, embeddings, retrieval techniques, prompt engineering) • Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow • Lead, mentor, and develop a team of AI Engineers, fostering a collaborative and inclusive team environment • Identify and address skill gaps, and provide opportunities for professional development • Coordinate with stakeholders to gather requirements, set priorities, and define project timelines • Ensure projects align with overall business objectives and data strategy • Ensure data quality, integrity, and security across all data engineering projects • Identify opportunities for process improvements and drive initiatives to enhance the efficiency and effectiveness of data operations • Ability to build/drive reusable frameworks that can drive efficiency of the overall data system • Manages conversation with the client stakeholders to understand the requirement and translate it into technical outcomes Required Skills (Must have) Tech: • Experience of 6-10 years in development and deployment of scalable AI/ML solutions • Has strong execution knowledge of Data Modeling, Databases in general (SQL and NoSQL), software development lifecycle and practices, unit testing, functional programming, etc • Develop and implement advanced Generative AI solutions (LLMs, embeddings, retrieval techniques, prompt engineering) • Design and optimize Retrieval-Augmented Generation (RAG) solutions • Manage Databricks workflows, including job and cluster creation also Databricks API • Apply data structures and algorithms knowledge, including multiprocessing and optimization techniques • Utilize Python libraries (Pandas, Numpy, FastAPI) for data processing and API development • Perform SQL optimization and database architecture design (schema creation, normalization, functions, triggers) • Deploy and orchestrate AI models using Docker and Kubernetes • Collaborate using GitHub for version control and team collaboration • Work with cloud platforms for AI solution deployment and management (Azure, GCP, AWS) • Utilize PySpark for data processing (optional) • Basic understanding of CI/CD pipelines and deployment process Non-Tech: • Strong problem-solving skills with an ability to assess the financial impact of decisions, both in running the delivery team and delivering solutions to clients • Proficient in written and verbal communication and able to hold conversations with mid-management-level clients • Ability to recognize pragmatic alternatives vis-à-vis a perfect solution and get the delivery teams on board to pursue them, balancing time priorities with potential business impact • Strong people skills, including conflict resolution, empathy, communication, listening, and negotiation • Shows proficiency in providing technical guidance and provide leadership and mentorship to the delivery team • Self-driven with a strong sense of ownership Good to Have Skills • Familiarity with data visualization tools and techniques • Knowledge of data security and privacy practices • Understanding of data governance and compliance frameworks • Experience with graph databases and graph processing frameworks • Experience with data virtualization and data federation techniques • Proficiency in data profiling and data quality management