- Bangalore - Karnataka - Índia
ML Engineer
Key Responsibilities
- Design, develop, and deploy ML and LLM-powered applications into production
- Build end-to-end ML & GenAI pipelines (data → evaluation → deployment → monitoring)
- Develop agentic systems using LLMs (multi-step reasoning, tool usage, orchestration)
- Implement RAG (Retrieval-Augmented Generation) architectures using vector search
- Integrate LLMs with enterprise systems via APIs, tools, and function calling
- Apply prompt engineering, model selection, and evaluation strategies
- Optimize inference performance (latency, cost, throughput)
- Implement MLOps + LLMOps practices (CI/CD, versioning, governance)
- Monitor production systems for:
- Response quality and hallucination risks
- Data drift and prompt drift
- Cost, latency, and token utilization
- Build dashboards and monitoring solutions for ML/LLM systems (performance, usage, business KPIs)
- Work with Databricks platform for data processing, feature engineering, and pipeline orchestration
- Implement guardrails, safety filters, and responsible AI controls
Key Competencies
- Strong systems thinking: ability to design production-grade AI systems
- Ability to balance experimentation with cost, latency, and reliability constraints
- Critical understanding of LLM limitations (hallucinations, grounding, context limits)
- Ability to translate model outputs into business-facing insights via dashboards
- Effective cross-functional collaboration