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TD Newton
About the role
Roles & responsibilities
Here are some of the key responsibilities of an AI Engineer SA1 :
1.Design and develop AI Application with strong frontend and backend skills. Deploy, maintain, and support application across all the environments, ensuring seamless integration with front-end and back-end systems.
2.Ability to scale up the deployed applications using container
services, strong experience in building the pipeline to meet the
performance benchmarks.
3.Conduct business analysis to gather requirements and develop
scripts, and pipelines that meet technical specifications for business
needs, utilizing both server-side and client-side technologies.
4.Develop real-time data ingestion and stream-analytic solutions
utilizing technologies such as Python (preferably Spark clusters), and
cloud platforms to support AI applications. Utilize multiple
programming languages and tools, including Python Java, and frontend
frameworks, preferably Angular to build prototypes for AI models and
evaluate their effectiveness and feasibility.
5.Develop application systems that adhere to standard software
development methodologies, ensuring robust design, programming,
backup, and recovery processes to deliver high-performance AI
solutions across the full stack.
6.Provide system support as part of a team rotation, collaborating
with other engineers to resolve issues and enhance system performance,
including both front-end and back-end components.
7.Operationalize open-source AI and data-analytic tools for
enterprise-scale applications, ensuring they align with organizational
needs and user interfaces.
8.Ensure compliance with data governance policies by implementing and
validating data lineage, quality checks, and data classification in AI
projects.
9.Understand and follow the company’s software development lifecycle
to effectively develop, deploy, and deliver AI solutions.
10.Design and develop AI frameworks leveraging open-source tools and
advanced data processing frameworks, integrating them with user-facing
applications. Lead the design and execution of complex AI projects,
ensuring alignment with ethical guidelines and principles under the
guidance of senior team members.
Mandatory technical & functional skills
•Strong programming skills on Python and hands on experience in
building backend services with frameworks like FastAPI, Flask, Django.
•E
xperience with prompt engineering and building AI applications using
frameworks like LangChain/LlamaIndex /LlamaPrase/LlamaCloud/Semantic
Kernel etc.
•Developed analytical/modeling solutions using variety of commercial
and open-source tools. Strong experience with Machine learning, Deep
Learning algorithms, using various frameworks Pytorch/Tensorflow etc
•Full-Stack Development: Proficiency in front-end and back-end
technologies, including JavaScript frameworks (e.g., React, Angular),
to build and integrate user interfaces with AI models and data
solutions.
Preferred technical & functional skills
•Data Management: Design, implement, and manage AI-driven data
solutions on the Microsoft Azure cloud platform, ensuring scalability
and performance.
•Technical Skills: Strong proficiency in Databricks/Fabric/Spark
Notebooks, SQL/NoSQL databases, Redis, and other data engineering
tools, as well as familiarity with
•Develop real-time data ingestion and stream-analytic solutions
leveraging technologies such as Kafka, Apache Spark (SQL, Scala,
Java), Python and Hadoop Platform and any Cloud Data Platform.
•Big Data Processing: Utilize big data technologies such as Azure
Databricks and Apache Spark to handle, analyze, and process large
datasets for machine learning and AI applications.
•Certifications: Relevant certifications such as Microsoft Certified:
Azure Data Engineer Associate, Azure AI Engineer or any other cloud
certification are a plus.
Key behavioral attributes/requirements
•Collaborative Learning: Open to learning and working with others.
•Project Responsibility: Able to manage project components beyond
individual tasks.
•Business Acumen: Strive to understand business objectives driving
data needs.
Educational qualifications
-Bachelor’s / Master’s degree in Computer Science
Work experience
•3 - 5 Yrs ( relevant exp min 2)