Job Description

Roles & responsibilities

Here are some of the key responsibilities of an 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 technical & functional skills

•Strong programming skills on Python/R, Java and hands on experience in building backend services with frameworks like FastAPI, Flask, Django. •Experience 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. •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.

Roles & responsibilities

Here are some of the key responsibilities of an 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 technical & functional skills

•Strong programming skills on Python/R, Java and hands on experience in building backend services with frameworks like FastAPI, Flask, Django. •Experience 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. •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.

This role is for you if you have the below

Educational qualifications

-Bachelor’s / Master’s degree in Computer Science

Work experience

3-5 years of experience (min 2 years of experience in AI applications)