Senior ML Platform Engineer (MLE and MLops)

Senior ML Platform Engineer (MLE and MLOps)

Experience: 6-10 Years

Location: Remote (Need local candidate from Noida or Bangalore)

Employment Type: Contract

Job Overview

We are looking for a Senior ML Platform Engineer with strong expertise in Machine Learning Engineering and MLOps to join our dynamic team. The candidate should have a solid background in deploying production-grade ML/DL models and building scalable MLOps pipelines. Remote work is allowed, but candidates must be based in or around Noida or Bangalore. A LinkedIn profile is mandatory for application and vetting.

Key Responsibilities

Model Packaging & Serving: Containerize and optimize models (ONNX/TorchScript) for batch and real-time inference using Azure ML, Vertex AI, AKS/GKE, or TF Serving/TorchServe/Triton. Implement rollout strategies (A/B, canary, shadow, blue/green) with rollback controls.

Pipelines & Reproducibility: Develop reusable training and inference pipelines with Azure ML Pipelines, Vertex AI Pipelines, or Kubeflow. Enforce reproducibility via MLflow (tracking/registry) and DVC (dataset versioning).

Features & Data: Manage feature stores (Feast/Tecton) across ADLS/GCS, Synapse/BigQuery. Collaborate with Data Engineering on scalable ETL/ELT using ADF, Dataflow, and Databricks/Spark/Ray.

Observability & Reliability: Instrument services with Prometheus, Grafana, and OpenTelemetry; define SLOs for latency and throughput. Monitor drift, bias, and performance using Evidently, WhyLabs, or Arize with alerting and runbooks.

CI/CD & Automation: Implement CI/CD for code, data, and models using GitHub Actions, Azure DevOps, or Cloud Build. Automate infrastructure with Terraform/IaC and enforce policy-as-code.

Security & Compliance: Manage IAM/RBAC, secrets (Key Vault, Secret Manager), artifact signing, and PII controls; maintain audit trails.

Activation & Integration: Deliver predictions to Salesforce/Gainsight via APIs or connectors; enable reverse-ETL and event-driven workflows. Publish curated outputs to BI semantic layers (Power BI, Looker, Tableau).

Cost & Performance: Optimize compute (CPU/GPU), autoscaling, and caching; track cost per 1K predictions and batch efficiency.

Required Skills

6–10+ years in ML Engineering, MLOps, or Platform Engineering with multiple production services shipped

Proficiency in Python (FastAPI/Flask), SQL, PyTorch, TensorFlow, Bash, Git

Experience with Azure ML, Vertex AI, Kubeflow, TorchServe, TF Serving, Triton, ONNX, TorchScript

Skilled in data and distributed computing using Spark, Ray, Databricks, BigQuery, Synapse

Expertise in MLflow, DVC, Feast, Tecton for tracking, versioning, and feature stores

Cloud and orchestrations skills with AKS, GKE, Docker, Kubernetes, Azure Data Factory, GCP Dataflow

Strong CI/CD and IaC knowledge with GitHub Actions, Azure DevOps, Terraform, Helm

Familiarity with Prometheus, Grafana, OpenTelemetry, ELK stacks for monitoring and observability

Experience with API integrations and reverse ETL workflows

Job Type: Contractual / Temporary
Contract length: 6 months

Pay: ₹100,000.00 - ₹120,000.00 per month

Work Location: Remote