Manager (AI Hub - GDC)

Role overview

Manager (AI Hub - GDC) at KPMG

The ideal candidate should have a strong background in working or developing agents using langgraph, autogen, and CrewAI.

Responsibilities

• Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams. 2.
• Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications.
• Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications. 5.
• Ensure the implementation supports scalability, reliability, maintainability, and security best practices. 9.
• Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices.

Required qualifications

• Mandatory technical & functional skills
• 8 to 11 Years of Experience

Preferred qualifications and skills

• Preferred technical & functional skills
• Understand business objectives and functions to support data needs This role is for you if you have the below Educational qualifications - Bachelor’s/Master’s degree in Computer Science - Certifications in Cloud technologies (AWS, Azure, GCP) and TOGAF certification (good to have) Work

Employment details

• Location: Hyderabad [Telangana] [India]

Application details

• Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization. 11.

Additional role details

• Roles & responsibilities Here are some of the key responsibilities of AI Tech Lead: 1.
• Design end-to-end AI applications, ensuring integration across multiple commercial and open source tools. 3.
• Partner with product management to design agile project roadmaps, aligning technical strategy.
• Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources. 4.
• Fast adaptability with the emerging technologies and methodologies, recommending proven innovations. 6.
• Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems. 7.
• Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems.
• Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance. 8.
• Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints.
• This includes project management defining project goals, allocating resources, and managing risks. 10.
• Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation. 12.
• Provide mentorship to engineering teams and foster a culture of continuous learning. 13.
• Proficiency in Python, with robust knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, and Keras.
• Understanding of Deep learning and NLP algorithms – RNN, CNN, LSTM, transformers architecture etc.
• Proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions.
• Hands-on skills with containerization (Docker) and orchestration frameworks (Kubernetes), including related DevOps tools like Jenkins and GitLab CI/CD.
• Experience using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation to automate cloud deployments.
• Proficient in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra) to manage structured and unstructured data.
• Expertise in designing distributed systems, RESTful APIs, GraphQL integrations, and microservices architecture. - Knowledge of event-driven architectures and message brokers (e.g., RabbitMQ, Apache Kafka) to support robust inter-system communications.
• Familiarity with open source model libraries such as Hugging Face Transformers, OpenAI’s API integrations, and other domain-specific tools.
• Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops
• Training and fine tuning of Large Language Models or SLMs (PALM2, GPT4, LLAMA etc )
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance.
• Key behavioral attributes/requirements
• Ability to mentor junior developers
• Ability to own project deliverables and contribute towards risk mitigation
• Understand business objectives and functions to support data needs Roles & responsibilities Here are some of the key responsibilities of AI Tech Lead: 1.