About the Role

Rubiscape’s 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 Rubiscape’s
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 India’s 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

· 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.