Senior ai engineer

The Math Company or Math Co 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, Math Co builds custom AI and advanced analytics solutions focused on enterprise problem-solving through its innovative hybrid model. Nucli OS, Math Co'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 Math Co a great place to work. We provide exciting growth opportunities, value capabilities, and attitude over experience, enabling the Mathemagicians to 'Leave a Mark'.ResponsibilitiesResponsible for designing, building, and maintaining scalable AI solutionsDevelop 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 growLead, mentor, and develop a team of AI Engineers, fostering a collaborative and inclusive team environmentIdentify and address skill gaps, and provide opportunities for professional developmentCoordinate with stakeholders to gather requirements, set priorities, and define project timelinesEnsure projects align with overall business objectives and data strategyEnsure data quality, integrity, and security across all data engineering projectsIdentify opportunities for process improvements and drive initiatives to enhance the efficiency and effectiveness of data operationsAbility to build/drive reusable frameworks that can drive efficiency of the overall data systemManages conversation with the client stakeholders to understand the requirement and translate it into technical outcomesRequired Skills (Must have)Tech:Experience of 4.5 - 7 years in development and deployment of scalable AI/ML solutionsHas strong execution knowledge of Data Modeling, Databases in general (SQL and No SQL), software development lifecycle and practices, unit testing, functional programming, etcDevelop and implement advanced Generative AI solutions (LLMs, embeddings, retrieval techniques, prompt engineering)Design and optimize Retrieval-Augmented Generation (RAG) solutionsManage Databricks workflows, including job and cluster creation also Databricks APIApply data structures and algorithms knowledge, including multiprocessing and optimization techniquesUtilize Python libraries (Pandas, Numpy, Fast API) for data processing and API developmentPerform SQL optimization and database architecture design (schema creation, normalization, functions, triggers)Deploy and orchestrate AI models using Docker and KubernetesCollaborate using Git Hub for version control and team collaborationWork with cloud platforms for AI solution deployment and management (Azure, GCP, AWS)Utilize Py Spark for data processing (optional)Basic understanding of CI/CD pipelines and deployment processNon-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 clientsProficient in written and verbal communication and able to hold conversations with mid-management-level clientsAbility 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 impactStrong people skills, including conflict resolution, empathy, communication, listening, and negotiationShows proficiency in providing technical guidance and provide leadership and mentorship to the delivery teamSelf-driven with a strong sense of ownershipGood to Have SkillsFamiliarity with data visualization tools and techniquesKnowledge of data security and privacy practicesUnderstanding of data governance and compliance frameworksExperience with graph databases and graph processing frameworksExperience with data virtualization and data federation techniquesProficiency in data profiling and data quality management