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TD Newton
Assistant Manager\SA2 : AI Tech Lead (AI Hub)
KPMG · posted 5 months ago
Hyderabadtechnology consulting
About the role
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.
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 candidate should have a strong background in working or developing
agents using langgraph, autogen, or 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.
•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
•Hands-on skills with containerization (Docker) and orchestration
frameworks (Kubernetes), including related DevOps tools like Jenkins and
GitLab CI/CD.
•Proficient in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB,
Cassandra) to manage structured and unstructured data.
•Experience using Infrastructure as Code (IaC) tools such as Terraform
or CloudFormation to automate cloud deployments.
•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
Responsibilities
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.
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 candidate should have a strong background in working or developing
agents using langgraph, autogen, or 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.
•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
•Hands-on skills with containerization (Docker) and orchestration
frameworks (Kubernetes), including related DevOps tools like Jenkins and
GitLab CI/CD.
•Proficient in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB,
Cassandra) to manage structured and unstructured data.
•Experience using Infrastructure as Code (IaC) tools such as Terraform
or CloudFormation to automate cloud deployments.
•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
Educational qualifications
-Bachelor’s/Master’s degree in Computer Science
-Certifications in Cloud technologies (AWS, Azure, GCP)
Work experience: 5-8 Years of Experience