Build and Maintain distributed, scalable, and reliable data pipelines that ingest and process data at scale and in real-time Create metrics and apply business logic using Spark, Scala Model, design, develop, code, test, debug, document and deploy application production through standard processes Harmonize, transform, and move data from a raw format consumable, curated views Analyze, design, develop, and test applications Contribute the maturation of Data Engineering practices, which may include providing training and mentoring to others Troubleshooting application issues by writing code, compiling, debugging, analyzing and reviewing complex application codeGenAI Platform Engineering &

- LLMOps: Production-grade platform architecture, model gateways, guardrails, and lifecycle management

- Agentic AI &
- Multi-Agent Orchestration: Workflow orchestration, ReAct-based reasoning,



intent classification (temporal knowledge is valuable to have) for automated governance and conversational workflows
- LLM Gateway &
- Model Serving (LiteLLM, OpenRouter): Unified access across Azure OpenAI, Bedrock, and Gemini with routing and spend governance
- Ops Side: Any experience with CI/CD tools is fine (e.g. Harness, Jenkins);

experience with Kubernetes is a plus

- Observability, Security &

- Governance: Experience with tools like Dynatrace and Datadog
- Cross-Functional Leadership and Platform Support: Owns client query handling, supports the platform deployed in production, clears support tickets from queues, and onboards users/apps onto the AI platform.

📌 Spark Scala (Hyderabad)
🏢 Quess Corp
📍 Hyderabad