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TD Newton
About the role
Roles & responsibilities
Here are some of the key responsibilities of a Sr. Software Engineer
(SA1) :
1.Design and develop AI-driven data ingestion frameworks and
real-time processing solutions that enhance data analysis and
machine learning capabilities across the full technology
stack.
2.Deploy, maintain, and support application codes and machine
learning models in production environments, ensuring seamless
integration with front-end and back-end systems.
3.Create and enhance AI solutions that facilitate seamless
integration and flow of data across the data ecosystem, enabling
advanced analytics and insights for end users.
4.Conduct business analysis to gather requirements and develop ETL
processes, scripts, and machine learning pipelines that meet
technical specifications and business needs, utilizing both
server-side and client-side technologies.
5.Develop real-time data ingestion and stream-analytic solutions
utilizing technologies such as Kafka, Apache Spark (SQL, Scala,
Java), Python, and cloud platforms to support AI applications.
Utilize multiple programming languages and tools, including Python,
Spark, Hive, Presto, Java, and JavaScript frameworks (e.g., React,
Angular) to build prototypes for AI models and evaluate their
effectiveness and feasibility.
6.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.
7.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.
8.Operationalize open-source AI and data-analytic tools for
enterprise-scale applications, ensuring they align with
organizational needs and user interfaces.
9.Ensure compliance with data governance policies by implementing
and validating data lineage, quality checks, and data classification
in AI projects.
10.Understand and follow the company’s software development
lifecycle to effectively develop, deploy, and deliver AI
solutions.
11.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
•Technical Skills: Strong proficiency in on Python as well as
familiarity with
machine learning frameworks (e.g., TensorFlow, PyTorch).
•In depth knowledge on ML, Deep Learning and NLP algorithms.
•Strong programming skills hands on experience in building backend
services with frameworks like FastAPI, Flask, Django, 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.
•Data Integration: Develop and maintain data pipelines for AI
applications, ensuring efficient data extraction, transformation,
and loading (ETL) processes
Strong oral and written communication skills with the ability to communicate technical and non-technical concepts to peers and stakeholders
Preferred technical & functional skills
•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.
•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.
•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
2+ years relevant experience of Data Scientist