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Senior Software Engineer (Machine learning Platform Engineering )

2 weeks, 5 days ago
India
Full-time
Senior
Software Engineer
Software Development
Apache Airflow Apache Spark AWS CI/CD Docker Feature Engineering Flink Go GraphQL gRPC Java Kafka Kubeflow Kubernetes LightGBM LLM Machine Learning Microservices MLflow Neo4j Python REST API Scala XGBoost
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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 7

Description

  • Build and operate the LLM control plane and gateway, including smart routing, rate limits, failover, and token/cost tracking.
  • Ship unified APIs and SDKs with normalized schemas, structured outputs, caching, and full observability across traces, logs, and metrics.
  • Enforce safety and privacy controls such as content filtering, prompt and response validation, and PII redaction.
  • Enable multi-model and multi-vendor LLM usage with automated canarying and versioning.
  • Own the agent runtime, including tool registry, permissions, function calling, grounding, and retrieval.
  • Design agent orchestration patterns 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 data ingestion pipelines to serve real-time context with access controls and lineage.
  • Implement hybrid retrieval using graph, vector, and keyword search with smart caching to balance accuracy, latency, and cost.
  • Define and manage SLOs for latency, uptime, and cost, while enabling autoscaling and spend controls.
  • Maintain model and agent registries with versioning, approvals, audit trails, reproducibility, and compliance support.
  • Provide templates, CLIs, sandboxes, and documentation to help product teams ship quickly, and mentor engineers on MLOps and AI safety best practices.

Requirements

  • 5+ 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 deployments, and online feature computation with Spark, Flink, or Kafka.
  • Experience with cloud and containers, especially AWS, Docker, and Kubernetes.
  • Practical ML knowledge covering 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, including provider adapters, routing policies, caching, quotas, rate limits, and cost/token accounting.
  • Experience with agentic systems, including tool use or function calling, orchestration frameworks, human-in-the-loop workflows, safety guardrails, and online evaluation or telemetry.
  • Experience with graph and retrieval systems, such as Neo4j, Neptune, TigerGraph, GraphQL, pgvector, Qdrant, or Milvus.
  • Preferred mindset includes platform-as-product thinking, strong observability and access-control habits, cost awareness, vendor-agnostic design, and the ability to document and teach complex systems.

Interested in this position?

Apply directly on the company website

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