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TD Newton
About the role
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Proposed designation: SA2
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Role type:
Individual contributor
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Reporting to:
Manager / Associate
Director
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Geo to be supported:
US
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Work timings:
2:00 PM to 10:30 PM
Here are some of the key responsibilities of a AI Engineer :
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 Skills:
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Strong programming skills on Python/R, Java and hands on experience in
building backend services with frameworks like FastAPI, Flask, Django.
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Experience with prompt engineering and building AI applications using
frameworks like LangChain/LlamaIndex /LlamaPrase /LlamaCloud/ Semantic
Kernel etc.
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Developed analytical/modeling solutions using variety of commercial and
open-source tools.
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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.
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Data Management: Design, implement, and manage AI-driven data solutions on
the Microsoft Azure cloud platform, ensuring scalability and performance.
Prefrred Skills:
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Technical Skills: Strong proficiency in Databricks/Fabric/Spark Notebooks,
SQL/NoSQL databases, Redis, and other data engineering tools, as well as
familiarity with
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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.
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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.
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Certifications: Relevant certifications such as Microsoft Certified: Azure
Data Engineer Associate, Azure AI Engineer or any other cloud
certification are a plus.