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Job Description: Manager AI Engineer / Data Scientist

Role Overview:

We are seeking an accomplished and visionary Manager Data Scientist with minimum 7 Years of experience in Data Science and Machine learning, preferable experience around NLP, Generative AI, LLMs, MLOps, Optimization techniques and AI solution Architecture to lead our AI team and drive the strategic direction of AI initiatives. In this role you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise and leadership skills. The ideal candidate should have a proven track record in AI leadership, a deep understanding of AI technologies, and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Minimum 7 Years of experience in Data Science and Machine learning. Excellent leadership skills with at least 2-3 years of people management OR technical architecture experience.

Responsibilities:

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, 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

Your people responsibilities:

Building a quality culture
Lead by example
Participating in the 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.

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