MLOps Engineer

":" About the Role

Rubiscapes RubiStudio studio promises enterprises a path from experiment to production in under 90 days and the MLOps Engineer is the person who makes that promise real. You will design the CI/CD pipelines, model registries, deployment orchestration, and monitoring infrastructure that keep hundreds of ML models running reliably across SaaS, BYOC, on-premises, and air-gap deployments. You will work closely with ML Engineers, Platform Engineers, and enterprise customer success teams to eliminate the gap between model training and business value.

Key Responsibilities

Build and maintain end-to-end ML pipelines using MLflow, Kubeflow, or Airflow that handle training, validation, packaging, and deployment of models at scale.

Design the model registry architecture within RubiStudio: versioning strategies, stage transitions (staging canary production), approval gates, and rollback mechanisms.

Implement automated model monitoring for data drift, concept drift, and prediction quality degradation, surfacing alerts into RubiSight operational dashboards.

Manage containerised model serving infrastructure (Docker + Kubernetes) across multi-cloud and on-premises deployment topologies aligned with Rubiscapes deployment flexibility.

Define and enforce MLOps best practices: reproducible experiments, environment parity, feature store integration, and audit-ready lineage for regulated-sector customers.

Collaborate with security and compliance teams to ensure model artefacts, training data references, and inference logs meet enterprise data governance standards.

Instrument inference endpoints with latency, throughput, and error-rate SLOs; own on-call response for production model degradation incidents.
Nice to Have

Experience operating ML infrastructure in air-gap or on-premises environments for government or defence customers.

Knowledge of feature stores (Feast, Tecton, or a custom implementation) and their integration into training and online inference paths.

Exposure to GPU cluster management and optimising inference throughput for large model serving.

Certification in AWS Machine Learning Specialty, Google Professional ML Engineer, or equivalent.

About Rubiscape

Rubiscape is Indias leading Decision Intelligence Platform, unifying data engineering, BI, machine learning, and agentic AI in a single governed platform. Built in Pune and trusted by Fortune 500 enterprises across BFSI, manufacturing, healthcare, and government. 8 international innovation patents. 10 Industry-Academia Labs COEs. From BI to AI One Platform. Every Decision.

Requirements Requirements

3+ years in MLOps, ML infrastructure, or ML platform engineering roles with demonstrable production deployments.

Proficiency with MLflow (or similar experiment tracking + registry tools) and workflow orchestration frameworks such as Airflow, Kubeflow Pipelines, or Prefect.

Strong container and Kubernetes skills: writing Helm charts, managing model-serving deployments, horizontal pod autoscaling for inference workloads.

Experience with at least one model-serving framework: TorchServe, Triton Inference Server, BentoML, or Seldon Core.

Working knowledge of Python and shell scripting sufficient to own pipeline code, not just configure GUI tools.

Familiarity with observability tooling (Prometheus, Grafana, OpenTelemetry) applied to ML workloads.

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