AI And DATA -Gen AI - Manager

Your technical responsibilities:

• Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions.
• Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance.
• Drive the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
• Collaborate with stakeholders to identify business opportunities, define AI project goals, and prioritize initiatives based on strategic objectives.
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
• Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases.
• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases.
• Ensure compliance with data privacy, security, and ethical considerations in AI applications.
• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.

Good to Have Skills :

• Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems.
• Experience on Optimization tools and techniques (MIP etc).
• Drive DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.
• Implement CI/CD pipelines and automate model deployment and scaling processes.
• Utilize tools such as Docker, Kubernetes, and Git for building and managing AI pipelines.
• Apply infrastructure as code (IaC) principles using tools like Terraform or CloudFormation.
• Implement monitoring and logging tools to ensure the performance and reliability of deployed AI models.
• Collaborate with software engineering and operations teams to ensure seamless integration and deployment of AI models.

Your client responsibilities:

• Work for managing the successful design, execution, and measurement of data initiatives across customer-facing engagements
• Communicate with internal stakeholders to make recommendations based on data
• Sort out business problems to translate into analytical questions to simplify and accelerate the solution development.
• Balancing excellent business communication skills with a deep analytical understanding is needed
• Run Scrum calls for team. Manage client delivery.
• Applying data Science, ML algorithms, using standard statistical tools and techniques for solving client business problems.
• Communicate and manage relationships with the onsite Program Manager.
• Regular status reporting to Management and onsite coordinators.
• Advocate for GDS work, work on innovative work/PoC’s and showcase to Onsite stakeholders to convince them to get more business.
• Interface with the customer representatives as and when needed
• Willing to travel to the customer’s locations on need basis within India and outside India.
• Willing to be flexible to work on various tools and technologies based on demand