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Senior Staff Software Engineer ( Machine learning Platform)

1 week, 3 days ago
India
Lead
Software Engineer
Software Development
Apache Airflow Apache Spark AWS CI/CD Docker Feature Engineering Flink Go GraphQL gRPC Java Kafka Kubeflow Kubernetes LLM Machine Learning Microservices MLflow MLOps Neo4j Python REST API Scala
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Tekion

Tekion

Tekion is a leading provider of cloud-native automotive platforms that unify DMS, CRM, Digital Retail, Analytics, and more. Their AI-powered software enables personalized selling, upsell, and cross-sell opportunities, driving revenue and profitability....

IT Services
1K-5K
Founded 2016
$435M raised
View All Jobs 4

Description

  • Build and operate the LLM control plane and gateway, including smart routing, rate limits, quotas, failover, and token and cost tracking.
  • Ship a unified API and SDKs using REST and gRPC with normalized schemas, structured outputs, caching, and full observability.
  • Enforce safety and privacy controls such as content filtering, prompt and response validation, and PII redaction.
  • Enable multi-model, multi-vendor LLM usage with automated canarying and versioning.
  • Own the agent runtime, including the tool registry, permissions, function calling, grounding, and retrieval.
  • Design orchestration patterns for agents and manage agent state and long-running workflows.
  • Build platform components for classical ML training and scoring pipelines, experiment tracking, and model packaging.
  • Monitor model and data drift and retrain or tune models to maintain accuracy and relevance.
  • Add human-in-the-loop review and safe actioning before agents interact with dealer systems.
  • Evolve the domain graph, entity resolution, and reliable data ingestion pipelines to serve real-time context to agents.
  • Define and maintain SLOs for latency, uptime, and cost, and enable autoscaling and spend controls.
  • Maintain a model and agent registry with versioning, approvals, audit trails, reproducibility, and compliance support.
  • Provide templates, CLIs, sandboxes, and documentation to help product teams build and ship quickly, while mentoring engineers on MLOps and AI safety.

Requirements

  • 12–15+ years building large-scale data, ML, or platform systems.
  • Strong software engineering fundamentals in API design, concurrency, and distributed systems.
  • Production experience with Python and one of Java, Scala, or Go.
  • Experience with microservices and API design.
  • Experience with MLOps at scale, including Airflow or Kubeflow, MLflow, CI/CD for models, A/B testing, shadow or canary releases, and online feature computation with Spark, Flink, or Kafka.
  • Experience with AWS is preferred, along with Docker and Kubernetes.
  • Practical ML knowledge including feature engineering, training, evaluation, and drift detection.
  • Experience deploying models that power user-facing workflows.
  • Experience building or operating an LLM gateway or control plane with provider adapters, routing policies, caching, quotas, rate limits, and cost and token accounting.
  • Experience with agentic systems, including tool use, function calling, orchestration frameworks, human-in-the-loop review, safety guardrails, and online evaluation or telemetry.
  • Experience with graph and retrieval technologies such as Neo4j, Neptune, TigerGraph, GraphQL, pgvector, Qdrant, or Milvus.
  • A platform-as-product mindset and ability to think in systems, with observability, fallback, and access control as core concerns.
  • Passion for AI and real-world LLM and agentic use cases.
  • A cost-aware approach that treats latency and dollars as first-class metrics.
  • Vendor-agnostic thinking and a focus on portability and resilience.
  • Strong documentation and teaching skills that help teams understand complex systems.

Interested in this position?

Apply directly on the company website

Apply Now

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