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