About the Role

As a Manager AI Engineer / Data Scientist, you will leverage your technical expertise and leadership skills to guide the design and implementation of AI systems. The ideal candidate will have a proven track record in AI leadership, a deep understanding of AI technologies, and experience in designing and implementing advanced AI models and systems. Proficiency in data engineering, DevOps, and MLOps practices is highly valued. A minimum of 8 years in Data Science and Machine Learning is required, along with 2-3 years of people management or technical architecture experience.

Responsibilities

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 and Agentic Frameworks, to develop innovative solutions for enterprise industry use cases.
• Integrate with relevant APIs and libraries, such as Azure OpenAI 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 with 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.

Client Responsibilities:

• Manage the successful design, execution, and measurement of data initiatives across customer-facing engagements.
• Communicate with internal stakeholders to make recommendations based on data.
• Translate business problems into analytical questions to simplify and accelerate solution development.
• Balance excellent business communication skills with a deep analytical understanding.
• Run Scrum calls for the team and manage client delivery.
• Apply data science and ML algorithms using standard statistical tools and techniques for solving client business problems.
• Communicate and manage relationships with the onsite Program Manager.
• Provide regular status reporting to Management and onsite coordinators.
• Advocate for GDS work, work on innovative projects/PoCs, and showcase to Onsite stakeholders to drive business growth.
• Interface with customer representatives as and when needed.
• Willing to travel to customer locations as needed, within India and internationally.
• Be flexible to work on various tools and technologies based on demand.

People Responsibilities:

• Build a quality culture.
• Lead by example.
• Participate in organization-wide people initiatives.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus.
• Proven experience in leading and managing AI projects and teams, with a focus on generative AI and LLMs.
• In-depth knowledge of machine learning, deep learning, and generative AI techniques.
• Proficiency in programming languages such as Python, R and frameworks like TensorFlow or PyTorch.
• Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.
• Familiarity with computer vision techniques for image recognition, object detection, or image generation.
• Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.
• Expertise in data engineering, including data curation, cleaning, and preprocessing.
• Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems.
• Experience in DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.
• Familiarity with tools such as Docker, Kubernetes, and Git for building and managing AI pipelines.
• Proficiency in implementing CI/CD pipelines and automating model deployment and scaling processes.
• Understanding of infrastructure as code (IaC) principles and experience with tools like Terraform or CloudFormation.
• Knowledge of monitoring and logging tools to ensure the performance and reliability of deployed AI models.
• Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models.
• Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions.
• Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels.
• Understanding of data privacy, security, and ethical considerations in AI applications.
• Track record of driving innovation and staying updated with the latest AI research and advancements.
• Ability to think strategically, identify business opportunities, and align AI initiatives with organizational objectives.