Senior AI Engineer

Overview

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’.

Roles & Responsibilities-

• 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
  
  

• Experience of 4.5 - 7 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
  
  

• 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
  
  

• 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