AI Software Developer in Rāniganj, India

Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
SAP BTP Datasphere
Minimum 3 Year(s) Of Experience Is Required
Educational Qualification : 15 years full time education
AI Powered Tech Talent
Build AI native, data centric products on SAP BTP Datasphere by combining strong enterprise data warehousing and semantic modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to move beyond dashboards into intelligent data experiences—data agents, conversational analytics, and grounded insights—built on governed Datasphere models and integrated enterprise sources.
SAP Datasphere is positioned as a data warehousing solution with integration capabilities.
AI Native Data Product Engineering (on Datasphere)

Design and implement governed data products using Datasphere concepts such as Spaces and shareable models/views, enabling teams to explore, transform, and share curated datasets across domains.
Build semantic models that are fit for both analytics and AI consumption (clear entity definitions, measures, hierarchies, lineage-friendly design).
~ Retrieval + Grounding (RAG) over Enterprise Data

Create grounded AI experiences by connecting LLM applications to Datasphere s curated models and enterprise sources (SAP and non SAP), ensuring responses are traceable to governed data.
Engineer retrieval strategies that respect domain boundaries (spaces), freshness needs, and access controls, so AI outputs remain reliable and compliant.
~ Hybrid Modernization & Migration (BW bridge patterns)

Enable transition paths from legacy warehouse investments by leveraging approaches such as reusing SAP BW models and skills with Datasphere / BW bridge, supporting phased cloud modernization.
~ Lakehouse style Layering & Data Quality by Design

Bronze/Silver/Gold) to land raw data, cleanse/validate, and publish analytics ready models—while maintaining clear rules for what s exposed for consumption.
Embed quality controls, validation checks, and reproducible transformations as part of the delivery lifecycle.
~ Build data agents that can plan, call tools (query/metadata/lineage), retrieve context, and generate answers with citations—backed by deterministic checks and fallback behaviors.
Evaluation, Observability & Responsible AI

Establish offline/online evaluation loops (golden questions, regression suites, behavior tests) for conversational analytics and data agents.
Add telemetry for AI interactions (latency, grounding rate, failure modes) to improve reliability and cost efficiency.
~ Partner closely with business, data governance, and platform teams to align data products with real decisions and operational workflows.
Primary Skills (AI Native Must Have)
SAP BTP Datasphere: data modeling, spaces, sharing patterns, enterprise semantic design.
Strong data warehousing fundamentals and ability to translate business domains into governed analytical models.
Hands-on building with LLMs + RAG (retrieval, grounding, prompt/tool design, evaluation).
Solid software engineering fundamentals: testability, CI/CD mindset, reliable integrations.
Migration/modernization experience leveraging BW bridge style transition patterns.
Layered architecture implementation (Bronze/Silver/Gold) for scalable analytics delivery.
Familiarity with vector search / embedding pipelines (when integrating external AI retrieval components).
Training foundation models from scratch (the emphasis is on building agentic apps and governed retrieval on enterprise data).
AI assisted only delivery this role owns the AI behavior (grounding, evaluation, safety) end to end.
Faster path from data to decision through conversational + agentic analytics grounded in governed Datasphere models.
Scalable modernization of hybrid data estates via patterns like BW bridge.
Higher trust AI outputs by implementing layered quality + evaluation loops.
A 15 years full time education is required