Roles & responsibilities

Here are some of the key responsibilities of AI Tech Lead:

1.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.Design end-to-end AI applications, ensuring integration across multiple commercial and open source tools.

3.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.

4.Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.

5.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.Ensure the implementation supports scalability, reliability, maintainability, and security best practices.

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

11.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.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

•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.

•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.

Preferred technical & functional skills

•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