MLOps Engineer

Rubiscape; Pune District; On-site; INR 900,000 - 1,400,000; Full time

Job summary

Rubiscape’s RubiStudio enables enterprises to move from experiment to production in under 90 days. The MLOps Engineer will design and operate CI/CD pipelines, model registries, and deployment orchestration across SaaS, on-premises, and air-gap deployments to ensure hundreds of ML models run reliably.

You will work with MLEngineers, Platform Engineers, and customer success teams to close the gap between training and business value, building robust monitoring and governance around model artefacts

Qualifications

• 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.

Responsibilities

• Build and maintain end-to-end ML pipelines and deployments at scale.
• Design model registry architecture with versioning, staging, canary, and production flows.
• Implement automated model monitoring for data drift and performance degradation.
• Manage containerised serving infrastructure (Docker + Kubernetes) across multi-cloud and on‑prem.
• Define and enforce MLOps best practices, including reproducible experiments and lineage.
• Collaborate with security and compliance teams on governance of artefacts and data.

Skills

MLOps

MLflow

Airflow

Kubernetes

Python

Shell scripting

Observability

Tools

TorchServe

Triton Inference Server

BentoML

Seldon Core

Job description

Rubiscape’s RubiStudio studio promisesenterprises a path from experiment to production in under 90 days — and theMLOps Engineer is the person who makes that promise real. You will design theCI/CD pipelines, model registries, deployment orchestration, and monitoringinfrastructure that keep hundreds of ML models running reliably across SaaS,BYOC, on-premises, and air-gap deployments. You will work closely with MLEngineers, Platform Engineers, and enterprise customer success teams toeliminate the gap between model training and business value.

Key Responsibilities

• Build andmaintain end-to-end ML pipelines using MLflow, Kubeflow, or Airflow that handletraining, validation, packaging, and deployment of models at scale.
• Design themodel registry architecture within RubiStudio: versioning strategies, stagetransitions (staging → canary → production), approval gates, and rollbackmechanisms.
• Implementautomated model monitoring for data drift, concept drift, and predictionquality degradation, surfacing alerts into RubiSight operational dashboards.
• Managecontainerised model serving infrastructure (Docker + Kubernetes) acrossmulti-cloud and on-premises deployment topologies aligned with Rubiscape’sdeployment flexibility.
• Define andenforce MLOps best practices: reproducible experiments, environment parity,feature store integration, and audit-ready lineage for regulated-sectorcustomers.
• Collaboratewith security and compliance teams to ensure model artefacts, training datareferences, and inference logs meet enterprise data governance standards.
• Instrumentinference endpoints with latency, throughput, and error-rate SLOs; own on-callresponse for production model degradation incidents.

Nice to Have

• Experienceoperating ML infrastructure in air-gap or on-premises environments forgovernment or defence customers.
• Knowledgeof feature stores (Feast, Tecton, or a custom implementation) and theirintegration into training and online inference paths.
• Exposureto GPU cluster management and optimising inference throughput for large modelserving.
• Certificationin AWS Machine Learning Specialty, Google Professional ML Engineer, orequivalent.

About Rubiscape

Rubiscape is India’s leading DecisionIntelligence Platform, unifying data engineering, BI, machine learning, andagentic AI in a single governed platform. Built in Pune and trusted by Fortune500 enterprises across BFSI, manufacturing, healthcare, and government. 8international innovation patents. 10 Industry-Academia Labs & COEs. From BIto AI — One Platform. Every Decision.

Requirements

• 3+ yearsin MLOps, ML infrastructure, or ML platform engineering roles with demonstrableproduction deployments.
• Proficiencywith MLflow (or similar experiment tracking + registry tools) and workfloworchestration frameworks such as Airflow, Kubeflow Pipelines, or Prefect.
• Strongcontainer and Kubernetes skills: writing Helm charts, managing model-servingdeployments, horizontal pod autoscaling for inference workloads.
• Experiencewith at least one model-serving framework: TorchServe, Triton Inference Server,BentoML, or Seldon Core.
• Workingknowledge of Python and shell scripting sufficient to own pipeline code, notjust configure GUI tools.
• Familiaritywith observability tooling (Prometheus, Grafana, OpenTelemetry) applied to MLworkloads.