AI Data Engineer

• Endtoend data engineering for AI & analytics

• Design, build, and maintain scalable data pipelines and services that feed ML models, LLM/RAG solutions, and advanced analytics.

• Serve as the data backbone for AI products from raw ingestion through curated, analytics and modelready datasets.

Data integration & data stitching

• Act as a master at finding, joining, and reconciling data from disparate systems (CRM, billing, interaction/call data, clickstream, thirdparty sources, etc.).
• Resolve data quality issues, gaps, and inconsistencies; establish reliable, reusable data assets for AI and analytics teams.

Advanced data architecture & modeling

• Design and implement advanced data architectures (e.g., lakehouse, dimensional models, domain data products) to support AI and analytics at scale.
• Build strategic data models and solutions tailored for analytics, ML, and AI use cases (feature stores, RAG retrieval layers, training/inference datasets).
• Define and maintain data contracts, schemas, and standards that ensure consistency, performance, and ease of use.

Automation, orchestration & observability

• Use workflow/orchestration tools (e.g., Airflow, Dagster, cloudnative orchestrators) to automate repetitive tasks and complex data flows.
• Implement robust monitoring, alerting, and observability for pipelines, ensuring reliability, data quality, and clear SLAs.

Cloud platforms, performance & cost optimization

• Build solutions on Databricks, AWS, and Redshift that are secure, performant, and costefficient.
• Size, estimate, and predict costs for data solutions at scale; continuously optimize cloud spend through smart architecture, rightsizing, and tuning.

AIaware data engineering & partnership

• Partner closely with AI Engineers and Data Scientists to understand model and LLM requirements and translate them into data designs, features, and pipelines.
• Implement data patterns tailored to ML/LLM workloads (feature stores, training/validation sets, inference pipelines, vector indexes for RAG).

Leveraging AI for engineering productivity

• Use AIassisted coding tools and other AI capabilities to improve development speed, code quality, documentation, and testing.
• Stay current on AI tooling relevant to data engineering and incorporate it into daytoday workflows.

Data stewardship & business partnership

• Become the expert on enterprise data and its usage understanding sources, lineage, meaning, and business relevance.
• Work closely with product, analytics, and business stakeholders to ensure data assets align with how the business operates, measures performance, and makes decisions., * Frequent Internal Hackathons: Engage in dynamic competitions with exciting prizes to keep your skills sharp.
• Cultural Celebrations: Strengthen our familial bonds through shared celebrations, fostering a sense of community.
• Diverse Project Exposure: Work on a variety of projects across sectors like Healthcare, Banking, e-commerce, and Retail, collaborating with leading global brands.
• Centre of Excellence (COE): Benefit from technical guidance and upskilling opportunities provided by our team of technology experts, helping you navigate your career path.
• E-Learning Platform: Gain access to comprehensive e-learning platforms coupled with a robust mentorship program to enhance your skills.
• Open Door Policy: Embrace a culture of mutual support, respect, and open dialogue, promoting a collaborative work environment.

If you are passionate and excited about working in a fast-paced, innovative environment, we would love to hear from you!

• Bachelor s degree in Computer Science, Data Engineering, Information Systems, or a closely related technical field.
• Advanced degree is a plus.

Experience

• 8+ years of data engineering experience in largescale, production environments.
• 8+ years experience data modeling and building strategic data solutions for analytics and ML, and 3+ years providing data structures specifically for AI solutions (including LLM/RAG use cases).
• 8+ years experience finding, joining, and reconciling data from disparate systems (CRM, billing, interaction/call data, operational systems, thirdparty sources, etc.).
• 8+ years experience with advanced data architectures (e.g., lakehouse, dimensional models, domain data products) supporting AI and analytics at scale.
• 8+ years experience defining data contracts, schemas, and standards that ensure consistency, performance, and ease of use across teams and platforms.
• 5+ years experience with automation & orchestration, using workflow/orchestration tools (e.g., Airflow, Dagster, cloudnative orchestrators) to automate repetitive tasks and complex data flows, including robust monitoring, alerting, and observability with SLAs.

Technical skills

• Programming & data processing

• Expertlevel SQL for complex transformations, data reconciliation, and performance tuning.

• Strong Python skills for ETL/ELT, data pipelines, and integration with ML and AI workflows.

• Experience with Spark (preferably on Databricks) for largescale data processing.

Cloud & platforms

• Deep, handson experience with Databricks, AWS (e.g., S3, Glue, EMR/compute, Lambda, IAM), and Amazon Redshift.

Data architecture & governance

• Strong understanding of advanced data architecture design (lakehouse patterns, dimensional modeling, data vault, domainoriented data products).

• Solid grasp of data governance, data quality, lineage, and metadata practices.

• Experience in large, complex enterprises (Fortune 100 or similar), especially with highvolume transactional, interaction, or customer data.

• AI & ML awareness

• Working knowledge of AI/ML and LLM/RAG data requirements and coding patterns (e.g., feature stores, training/validation splits, vector stores, retrieval indexes).

• Experience collaborating closely with AI/ML teams and integrating with their pipelines and APIs.

Cost & performance

• Demonstrated ability to size, estimate, and optimize compute, storage, and data processing costs in cloud environments.
• Experience tuning queries, jobs, and architectures for both performance and cost efficiency.

Mindset & collaboration

• Strong ownership mentality; accountable for the reliability, quality, and fitness of the data you provide.
• Excellent collaboration skills; proven success partnering with AI Engineers, Data Scientists, and Analytics teams to deliver robust, Fortune 100grade solutions.
• Clear communicator who can explain data structures, constraints, and tradeoffs to both technical and nontechnical stakeholders.

Preferred Qualifications

• Experience with CI/CD, infrastructureascode, and DataOps/MLOps practices.

Familiarity with common analytics and BI tools (e.g., Tableau, Power BI, Looker) and how they consume data

R Systems is a leading digital product engineering company that designs and develops chip-to-cloud software products, platforms, and digital experiences that empower its clients to achieve higher revenues and operational efficiency. Our product mindset and engineering capabilities in Cloud, Data, AI, and CX enable us to serve key players in the high-tech industry, including ISVs, SaaS, and Internet companies, as well as product companies in telecom, media, finance, manufacturing, and health verticals. We Are Great Place to Work Certified in 10 countries with a full-time workforce [India, USA, Canada, Poland, Romania, Moldova, Indonesia, Singapore, Malaysia & Thailand]! We are recognized as one of the Best Tech Brands 2024 by the Times Group and India's Top 500 Value Creators 2023 by Dun & Bradstreet.