Roles & responsibilitiesHere are some of the key responsibilities of AI Tech Lead:
- 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.
- Design end-to-end AI applications, ensuring integration across multiple commercial and open source tools.
- Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. 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.
- Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.
- Fast adaptability with the emerging technologies and methodologies, recommending proven innovations.
- Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems.
- 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.
- Ensure the implementation supports scalability, reliability, maintainability, and security best practices.
- 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.
- Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization.
- Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation.
- Provide mentorship to engineering teams and foster a culture of continuous learning.
Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices.
Mandatory technical & functional skills
- Manage multiple projects and teams in parallel with strong cross-collaborative skills. Must have excellent troubleshooting skills.
- The ideal candidate should have a strong background in working or developing agents using langgraph, autogen, and CrewAI.
- Proficiency in Python, with robust knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, and Keras.
- Scientific understanding of Deep learning and NLP algorithms – RNN, CNN, LSTM, transformers architecture etc.
- Familiarity with open source model libraries such as Hugging Face Transformers, OpenAI’s API integrations, and other domain-specific tools. Strong understanding of generative techniques, including GANs, VAEs, diffusion models, and autoregressive models.
- Training and fine tuning of Large Language Models or SLMs (PALM2, GPT4, LLAMAetc )
- Proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions. Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops
- Hands-on skills with containerization (Docker) and orchestration frameworks (Kubernetes), including related DevOps tools like Jenkins and GitLab CI/CD.
- 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.
- Open-source contributions or published research in relevant domains.
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
Key leadership competencies
- Should drive engaged workforce and uphold positive relationships with employees, to foster a culture of collaboration, innovation, and inclusivity.
- Quality: Must have the ability to oversee all aspects of quality control processes and ensure that the services provided to the clients meet the highest possible standards by being detail-oriented, analytical, and highly organized.
- Continuous Improvement: Should be able to identify the areas of improvement and focus on continual improvement
- Financial Knowledge: Must possess strong financial skills to ensure that the financials
of their respective unit are managed to or better than budget.
- Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance.
Other information
- Interview process: Technical Interviews and HR Interview
- Does the job role involve travelling: : Yes (Frequency will be based on Business Requirements)
- Does the busy season apply to this role?: Yes
(At the time of Quarterly filings and Year end filings)
Responsibilities
Roles & responsibilitiesHere are some of the key responsibilities of AI Tech Lead:
- 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.
- Design end-to-end AI applications, ensuring integration across multiple commercial and open source tools.
- Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. 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.
- Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.
- Fast adaptability with the emerging technologies and methodologies, recommending proven innovations.
- Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems.
- 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.
- Ensure the implementation supports scalability, reliability, maintainability, and security best practices.
- 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.
- Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization.
- Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation.
- Provide mentorship to engineering teams and foster a culture of continuous learning.
Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices.
Mandatory technical & functional skills
- Manage multiple projects and teams in parallel with strong cross-collaborative skills. Must have excellent troubleshooting skills.
- The ideal candidate should have a strong background in working or developing agents using langgraph, autogen, and CrewAI.
- Proficiency in Python, with robust knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, and Keras.
- Scientific understanding of Deep learning and NLP algorithms – RNN, CNN, LSTM, transformers architecture etc.
- Familiarity with open source model libraries such as Hugging Face Transformers, OpenAI’s API integrations, and other domain-specific tools. Strong understanding of generative techniques, including GANs, VAEs, diffusion models, and autoregressive models.
- Training and fine tuning of Large Language Models or SLMs (PALM2, GPT4, LLAMAetc )
- Proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions. Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops
- Hands-on skills with containerization (Docker) and orchestration frameworks (Kubernetes), including related DevOps tools like Jenkins and GitLab CI/CD.
- 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.
- Open-source contributions or published research in relevant domains.
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
Key leadership competencies
- Should drive engaged workforce and uphold positive relationships with employees, to foster a culture of collaboration, innovation, and inclusivity.
- Quality: Must have the ability to oversee all aspects of quality control processes and ensure that the services provided to the clients meet the highest possible standards by being detail-oriented, analytical, and highly organized.
- Continuous Improvement: Should be able to identify the areas of improvement and focus on continual improvement
- Financial Knowledge: Must possess strong financial skills to ensure that the financials
of their respective unit are managed to or better than budget.
- Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance.
Other information
- Interview process: Technical Interviews and HR Interview
- Does the job role involve travelling: : Yes (Frequency will be based on Business Requirements)
- Does the busy season apply to this role?: Yes
(At the time of Quarterly filings and Year end filings)
Qualifications
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 must have TOGAF certification or any equivalent certification
Work experience:
14+ Years of Experience#KGS
