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TD Newton
About the role
•
Proposed designation:
Manager - AI Tech Lead
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Role type:
Individual contributor
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Reporting to: Associate
Director/ Director
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Geo to be supported:
US
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Work timings:
2:00 PM to 10:30 PM
| Skills | GTS Role | Mandatory Skills | Preferred Skills |
| AI Skills | AI Tech Lead | NLP, Gen AI, Deep Learning, Machine Learning | DevOps /MLOps /LLM Ops |
| LLM Architecture ( BERT, GPT, etc) | Fine Tuning of LLMs or SLMs (PALM2, GPT4, LLAMA etc ) | ||
| LangChain/LangGraph/LangSmith or LlamaIndex or LlamaCloud | |||
| Vector DBs (Chroma DB, FAISS, etc) | |||
| TensorFlow/PyTorch | |||
| Strong Application architect skills | |||
| Frontend frameworks - React, Angular | |||
| Cloud computing - GCP, AWS or Azure |
Here are some of the key responsibilities of AI architect:
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.
Educational qualifications
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Bachelor’s/Master’s degree in Computer Science
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Certifications in Cloud technologies (AWS, Azure, GCP) and TOGAF
certification (good to have)
Required Skills:
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The ideal candidate should have a strong background in working or
developing agents using langgraph, autogen, and CrewAI.
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Proficiency in Python, with robust knowledge of machine learning
libraries and frameworks such as TensorFlow, PyTorch, and Keras.
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Understanding of Deep learning and NLP algorithms – RNN, CNN, LSTM,
transformers architecture etc.
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Proven experience with cloud computing platforms (AWS, Azure, Google
Cloud Platform) for building and deploying scalable AI solutions.
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Hands-on skills with containerization (Docker) and orchestration
frameworks (Kubernetes), including related DevOps tools like Jenkins and
GitLab CI/CD.
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Experience using Infrastructure as Code (IaC) tools such as Terraform or
CloudFormation to automate cloud deployments.
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Proficient in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB,
Cassandra) to manage structured and unstructured data.
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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.
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Familiarity with open source model libraries such as Hugging Face
Transformers, OpenAI’s API integrations, and other domain-specific
tools.
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Large scale deployment of ML projects, with good understanding of DevOps
/MLOps /LLM Ops
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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