Tech Mahindra

Role & responsibilities :- Architect and develop end-to-end Gen AI and machine learning solutions, focusing on scalability, performance, and modularity, in collaboration with DDIT teams to ensure alignment with enterprise architecture standards.- Design, train, and fine-tune Large Language Models (LLMs) like GPT, Claude, or Cohere for various applications or pilots, working closely with DDIT to leverage existing data infrastructure and tools.- Optimize AI systems for deployment on cloud platforms such as AWS, Azure, Snowflake, or Databricks, ensuring high performance and cost efficiency, in partnership with DDIT to utilize best practices and cloud resources.- Develop APIs and integrate AI models into existing enterprise systems for seamless business adoption, coordinating with DDIT to ensure compatibility and security.- Collaborate with data scientists, software engineers, IDS colleagues, DDIT, and business teams to turn complex requirements into pilot AI solutions, ensuring cross-functional alignment and support.- Stay updated on the latest advancements in AI, especially in LLM architectures, generative AI, and model optimization techniques, and share insights with DDIT to foster continuous improvement and innovation.- Conduct technical reviews, ensure code quality, and enforce best practices in AI development and deployment, in collaboration with DDIT to maintain high standards and compliance.- Maintain the analytics workbench along with the data science team, ensuring integration with DDIT's data management and analytics platformsAdditional qualifications are as follows :- Advanced degree in Computer Science, Engineering, or a related field (PhD preferred).- 5+ years of experience in AI/ML engineering (data engineering could be appropriate depending on experience), with at least 2 years focusing on designing and deploying LLM-based solutions.- Strong proficiency in building AI/ML architectures and deploying models at scale.- Deep knowledge of LLMs and experience in fine-tuning and applying them in business contexts.- Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD pipelines - Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps tools for scalable deployment.- Solid understanding of distributed computing frameworks and system design principles.- Experience with API development, integration, and model deployment pipelines.- Strong problem-solving skills and a proactive, hands-on approach to challenges.- Innovation in Machine Learning Models Approaches : Delivers high quality machine learning models in an agreed upon time frame that meet their value-based use case objectives- Model Development and Incubation : Early detection of new models or approaches identified proactively- Model Explainability and Standards : Feedback from stakeholders on the transparency and clarity of model outputs in effectively implemented- Model Fine-Tuning and Optimization : Success rate of model fine-tuning efforts to enhance performance and scalability (ref:hirist.tech)