Senior Gen AI Engineer
At a glance
Senior AI Engineering role at Weekday AI. Remote, India ยท full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain data platforms and pipelines for batch and real-time processing
- Use Databricks and cloud services to process large datasets for analytics, reporting and AI applications
- Shape data models and improve workflows for speed, scale, reliability and cost
- Bring together structured and unstructured data, adding checks, monitoring and governance
- Turn business needs into data solutions, then troubleshoot and support them in production
What you bring
- At least five years in data engineering, analytics engineering, data platforms or a related technical role
- Hands-on Databricks experience and a track record building scalable data pipelines
- A solid grasp of data modelling, ETL/ELT and distributed processing
- Experience with a major cloud platform and programming in Python, SQL or another listed language
Who this fits
This senior, full-time role suits someone who can take data projects from requirements through implementation and production support. The position is remote in India.
From the employer
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ญ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฒ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ญ๐ฌ-๐ฒ๐ฑ ๐๐ฃ๐)
Experience: 5+ yrs
Location: India
Job Type: Full-time
As a Data Engineering and Analytics professional, you will work on designing, developing, and optimising modern data platforms that support analytics, business intelligence, and AI-driven use cases. You will collaborate with engineering, analytics, product, and business teams to build robust data solutions that can scale with evolving business requirements.
The role involves working with cloud platforms, Databricks, data pipelines, data processing, analytics, and modern data architectures. You will contribute to end-to-end data initiatives, from understanding requirements and designing solutions to implementation, optimisation, and production support.
Requirements
Key Responsibilities
- Design, develop, and maintain scalable data engineering and analytics solutions.
- Build reliable and efficient data pipelines for batch and real-time data processing.
- Develop data platforms and workflows using Databricks and modern cloud technologies.
- Work with large and complex datasets to support analytics, reporting, and AI-driven applications.
- Design data models and optimise data processing workflows for performance and scalability.
- Integrate data from multiple structured and unstructured sources.
- Implement data quality, validation, monitoring, and governance practices.
- Collaborate with data scientists, analysts, engineers, product teams, and business stakeholders.
- Translate business requirements into scalable technical and data solutions.
- Troubleshoot data pipeline, processing, performance, and integration issues.
- Optimise existing data architectures and workflows to improve reliability, efficiency, and cost.
- Contribute to cloud-based data architecture and platform modernisation initiatives.
- Develop reusable frameworks, components, and best practices for data engineering.
- Support the deployment, monitoring, and maintenance of data solutions in production environments.
- Stay current with emerging technologies in data engineering, cloud, analytics, AI, and modern data platforms.
What Makes You a Great Fit
- 5+ years of professional experience in data engineering, analytics engineering, data platforms, or a related technology role.
- Strong experience designing and developing scalable data pipelines and data processing solutions.
- Hands-on experience with Databricks and modern cloud-based data platforms.
- Strong understanding of data engineering concepts, data modelling, ETL/ELT, and distributed data processing.
- Experience working with one or more major cloud platforms such as AWS, Azure, or GCP.
- Strong programming and scripting skills in technologies such as Python, SQL, Scala, or Java.
- Experience working with relational and non-relational databases and large-scale datasets.
- Good understanding of data architecture, integration patterns, performance optimisation, and data quality.
- Experience supporting analytics, business intelligence, machine learning, or AI-driven use cases.
- Strong analytical and problem-solving skills with the ability to troubleshoot complex data challenges.
- Ability to work effectively with cross-functional and technical teams.
- Strong communication skills with the ability to explain technical concepts clearly to business stakeholders.
- Experience working in agile, fast-paced technology environments.
- Strong ownership mindset with the ability to independently drive projects from requirements through implementation and production.
- Passion for learning and experimenting with emerging data, cloud, analytics, and AI technologies.