Data & GenAI Engineer
Confirmed on the employer's own hiring board on Oct 5, 2026. First seen by Alion on Jun 17, 2026. Accuris scores B on the Alion truth index.
Senior Associate 1 – Data & GenAI Engineer
Location: Bangalore, IN
Experience: 3–5 years relevant industry experience
Why Work for Accuris
Accuris works on high-value, real-world data problems where reliability and correctness matter. You’ll build production-grade data and GenAI systems that power decision-support products used by global customers. This is not experimental AI — this is measurable, grounded, and scalable AI backed by robust data engineering.
Role Summary
You will design and own data pipelines and GenAI-driven services that transform messy enterprise data into high-quality datasets and production-ready product capabilities.
This role requires strong data engineering depth, with the ability to build and integrate backend APIs and contribute to frontend integrations when needed. You will work across data, application, and AI layers to ship scalable systems end-to-end.
What You’ll Do
Data Engineering
Design and own ETL/ELT pipelines (batch and streaming where required).
Drive Medallion architecture design (Bronze, Silver, Gold) with clear data contracts and quality checks.
Build scalable transformations using Spark/Databricks with performance tuning. Implement data validation frameworks, monitoring, lineage, and observability.
Handle schema evolution, incremental loads, idempotency, and orchestration patterns. Collaborate with content and application teams to define reliable data models.
GenAI Engineering
Design and improve production RAG systems: ingestion, chunking strategy, embedding pipelines, indexing, retrieval optimization.
Work with vector search systems: filtering, hybrid search, ranking improvements. Optimize prompt templates for grounding, structured outputs, and cost efficiency. Define evaluation metrics for retrieval quality, hallucination reduction, and latency. Contribute to architecture decisions around AI services and integration patterns.
Backend s Full-Stack Contributions
Design and implement backend APIs (FastAPI or similar frameworks). Build microservices to expose data products and AI capabilities.
Integrate APIs with frontend applications (basic Angular/JS understanding preferred). Work with authentication, API gateways, and service orchestration patterns.
Collaborate with frontend teams to ensure data contracts and performance alignment.
Must Have
3–5 years’ experience in Data Engineering / Backend Engineering. Strong Python and advanced SQL skills.
Hands-on Spark/Databricks experience with performance optimization.
Deep understanding of Medallion architecture and production data systems. Experience building reliable data pipelines with testing and monitoring.
Practical understanding of LLMs, RAG systems, embeddings, vector search. Experience building and deploying REST APIs.
Understanding of system design basics (scalability, reliability, trade-offs).
Nice to Have
Frontend exposure (React/Angular or similar).
Experience with API gateways and microservices architecture. Experience with CI/CD pipelines and containerization (Docker). Experience with evaluation frameworks for GenAI systems.
Knowledge of cost optimization in cloud-based data platforms.
Tech Stack
Python, SQL, Spark/Databricks, Delta Lake, orchestration tools, REST APIs (FastAPI), vector search systems, LLM APIs, Git, CI/CD, monitoring/logging, basic frontend frameworks.
Success Looks Like
You independently design and ship production-grade pipelines.
You improve RAG quality through measurable retrieval and prompt improvements. You build backend services that are scalable, observable, and secure.
You reduce data defects through better modeling and validation practices.
You proactively identify foundational gaps and address them before feature work scales.
Experience That Stands Out
Built and maintained a multi-layer medallion architecture at scale.
Designed and deployed a production RAG system with measurable evaluation. Built backend APIs serving AI/data features consumed by frontend apps.
Improved system performance through Spark optimization or query tuning. Contributed to cross-team architectural improvements.