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Mid-level ML platform role with generic title and Bangalore location increases applicant competition.
ML platform and MLOps skills are transferable across industries, though automotive domain graph adds specificity.
Explicit 2+ years and many mandatory skills like MLOps, LLM gateway, cloud, and observability.
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Own and operate the LLM control plane/gateway for multi-tenant workloads including routing, safety, quota management, and cost tracking.
Develop and maintain APIs and SDKs enabling agentic AI and classical ML model deployment with observability and safety guardrails.
Drive platform enhancements including data ingestion, context-serving, model monitoring, orchestration patterns, and offline/online evaluation impacting dealer KPIs like upsell and cycle time.
Minimum 2+ years experience building large-scale data/ML or platform systems with strong software engineering fundamentals.
Proficient in Python and at least one of Java, Scala, or Go; experience with microservices and API design.
Experience with MLOps pipelines, cloud platforms (AWS preferred), container technologies (Docker/Kubernetes), and multi-tenant SaaS performance/cost engineering.
Work Experience Required: Minimum 2+ years building large-scale data/ML or platform systems.
Experienced with building and operating LLM gateways/control planes with provider adapters, routing, caching, and cost/token tracking.
Strong knowledge of agentic systems involving orchestration frameworks, tool use, human-in-the-loop workflows, and safety guardrails.
Skilled in graph and hybrid retrieval systems (knowledge graphs and vector search), with a platform mindset focused on developer experience, system observability, and cost-efficiency.