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Strong company brand and Bangalore location but senior ML/AI niche reduces applicant density.
Specialized agentic AI and production ML requirements limit transferability across industries without similar experience.
Multiple mandatory senior AI, RAG, orchestration, cloud, and production requirements increase filtering.
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Design, build, and scale enterprise-grade multi-agent AI systems, including orchestration and context engineering, targeting business workflow automation.
Own end-to-end lifecycle of AgentOps solutions: intake, design, validation, deployment, monitoring, and continuous improvement.
Integrate AI powered systems with enterprise platforms, ensure observability, reliability, compliance, and optimize for cost and performance.
8–10+ years of software engineering experience with strong AI/ML exposure.
Bachelor's or Master's degree in computer science.
Proficiency in Python and backend engineering, experience with orchestration frameworks (e.g., CrewAI, LangGraph, AutoGen, MAF).
Experience deploying cloud-native AI services and familiarity with observability/monitoring tools (e.g., Application Insights, OpenTelemetry, Azure Monitor).
Experienced in building and deploying production-grade agentic AI systems, beyond prototypes.
Strong foundation and hands-on exposure to RAG, embeddings, vector search, and distributed agent communication patterns (MCP, A2A).
Comfortable working at the intersection of LLMs, platform design, and software engineering, with ownership across the full SDLC.