Senior Machine Learning Engineer

Tekion Corporation; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Tekion Corporation in Bengaluru seeks an experienced engineer to own the AI‑native platform powering its automotive suite. You will build the LLM control plane and gateway, define orchestration patterns, and enable safe, cost‑effective multi‑provider AI usage.

You will shape platform components for training and scoring pipelines, maintain governance, and ensure latency and reliability meet SLAs. Strong distributed systems knowledge is required.

Qualifications

• 5+ years building large‑scale data/ML or platform systems.
• Production Python experience with Java/Scala/Go.
• Experience with MLOps, pipelines, and model lifecycle.

Responsibilities

• Build and operate the LLM control plane and gateway.
• Design orchestration patterns and manage agent/runtime state.
• Ensure safety, privacy, and cost controls for multi‑provider LLM use.
• Enable scalable, multi‑tenant ML platforms and retrieval systems.
• Collaborate with product teams to ship AI features quickly and safely.

Skills

Large-scale data/ML systems

Python

Java/Scala/Go

MLOps at scale

AWS / Cloud

Docker/Kubernetes

Education

Tools

Docker

Kubernetes

MLflow

Airflow

Kubeflow

Job description

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting‑edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

Why This Role Matters

This role powersTekion’sAI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence.You’lloperationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes.You’llalso build the resilient control layer-MCP and the LLM Gateway-that enables safe, cost‑efficient, multi‑provider LLM usage. Finally,you’lldefine the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale.In addition to enabling agentic systems powered by LLMs, this role also drivesbuildingtheplatformforclassical ML models driving optimization across dealership operations.

What Makes This Opportunity Unique

This role offers direct, measurable impact on dealer outcomes and consumer experiences acrossTekion’sAutomotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and,qualityandcostguardrails.You’llleveragea rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation.You’llalso shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue. You’llalsomaintainand enhance the platform to support classical supervised and unsupervised MLmodels .

Responsibilities

Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking.

Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics).

Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction.

Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning.

Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval.

Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows.

Enabling platform components for training and scoring pipelines for classical ML (e.g.,XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging.

Create components toMonitormodel anddata drift, retraining and tuning models as needed tomaintainaccuracy and relevance.

Evolve the domain graph and entity resolution; build reliable data ingestion pipelines.

Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage.

Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost.

Run continuous offline/online evaluations for quality, factuality, bias, and safetyfor the platform sanity.

Define SLOs for latency (p50/p95), uptime, andcostviewcapabilities; enable autoscaling and spend controls.

Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support complianceswhere needed.

Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and championMLOpsand AI safety best practices.

Desired Skills & Experience

5+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems).

Production experience with Python plus one of Java/Scala/Go; microservices and API design.

MLOpsat scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka).

Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS.

Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows.

Built oroperatedan LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit,costand token accounting.

Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph),GraphQL, vector search (e.g.,pgvector/Qdrant/Milvus), hybrid retrieval patterns.

Preferred Mindset

Platform‑as‑product: obsess over developer experience, paved roads, and clearSLAs.

Thinks in systems-observability, fallback, access control are core, not afterthoughts.

Passionate about AI‑enjoys enabling real‑world LLM and agentic use cases.

Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation.

Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience.

Documentation and teaching: you make complex systems understandable; you uplevel teams.

Tekion is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, victim of violence or having a family member who is a victim of violence, the intersectionality of two or more protected categories, or other applicable legally protected characteristics.

For more information on our privacy practices, please refer to our Applicant Privacy Notice here .

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