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TD Newton
About the role
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
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)
Work experience: 5-8 Years of Experience