MLOps Platform Setup
Greenfield or migration MLOps platforms on SageMaker, Vertex AI, Azure ML, or Kubernetes, scoped against your data residency, latency, and cost posture.
- Reference architecture
- IaC modules
- Cost baseline
Specialist MLOps engineers for CI/CD/CT pipelines, model monitoring, retraining workflows, and cloud-native MLOps. Onboarded inside your VPC, on your stack, on your sprint cadence, from day one.
Trusted by enterprises across Retail, Manufacturing, BFSI, Logistics, and FMCG
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With 24+ years of enterprise delivery and a bench of 500+ elite engineers, orangemantra operates as a full-cycle MLOps partner that builds production ML pipelines backed by CI/CD/CT automation, model governance, and cloud-native engineering.
Most ML models ship once, then silently degrade. Hire MLOps engineers who own the production lifecycle: pipeline orchestration, drift detection, retraining triggers, inference cost control, and the audit trail regulators ask for. Paired with DevOps services and cloud-native engineering, your MLOps platform becomes a measurable operating system, not a one-time deployment.
Our Core MLOps Capabilities
Every engagement moves through these three stages. Hire MLOps engineers who own each layer end-to-end, not specialists who hand off after deployment.
Feature stores, data versioning, reproducible training pipelines, and experiment tracking. Every model artifact carries its data lineage, code commit, and hyperparameters from the first run.
CI/CD/CT pipelines move vetted artifacts through the model registry, into containerised serving on Kubernetes, with zero-downtime rollouts and automatic rollback when canary metrics regress.
Real-time drift detection, accuracy decay alerts, inference cost dashboards, and automated retraining triggers. Models that earn ROI stay in production. Models that degrade get rolled back, not papered over.
Pre-vetted MLOps engineers ready to start inside a fortnight. The bench covers SageMaker, Vertex AI, Azure ML, and Kubernetes without recruitment lag.
Models ship behind canary metrics, evaluation suites, and rollback triggers. A regression in P95 latency or accuracy reverses the deploy automatically.
Fluent across AWS, GCP, and Azure with Kubernetes underneath. The right stack for your data residency and cost posture, not a single-cloud preference.
A working MLOps platform inside three to six weeks, then a hardened path to scale with continuous training, drift alerts, and inference cost controls.
Audit-ready model registries, data lineage, and approval gates from day one. SOC 2, HIPAA, GDPR, and PCI DSS are the floor, not the ceiling.
If a model regresses at 2 am, the MLOps developers for hire are a Slack ping away. Coverage windows are set on the engagement, not on a generic SLA card.
The right answer depends on data residency, call volume, retraining frequency, and the cost posture your business can defend. Hire MLOps engineers who frame the trade-off before they provision infrastructure.
Best when speed-to-production matters and the team is small. Engineers stand up SageMaker, Vertex AI, or Azure ML with IaC, evaluation gates, and cost dashboards from day one.
Kubeflow, KServe, MLflow, and Prometheus on EKS, GKE, or AKS. Right for data residency and sustained workloads where managed pricing gets expensive.
Sensitive training on-prem, scale-out inference on managed cloud. One model registry, one observability stack, one cost dashboard across both planes.
Drift-triggered retraining, shadow deployment, and automatic promotion when evaluation passes. Pairs naturally with DevOps services for the underlying pipelines.
Model optimisation, quantisation, and over-the-air rollout for vehicles, IoT, and manufacturing equipment. Telemetry feeds back into the central registry.
Short, sharp engagements to audit an existing ML estate, surface reliability and cost risk, and produce a remediation plan you can act on next sprint.
Hire MLOps engineers who build for the line items finance can verify: inference cost per call, retraining cadence, drift caught before degradation, and time-to-rollback when a release goes wrong.
Explore your MLOps use caseAI's impact on business is undeniable and immeasurable. Gear up with the orangemantra MLOps engineering team.
The hiring path is built around enterprise procurement reality, not freelancer marketplaces. NDA on day one, profiles inside 48 hours, interviews on your schedule, and onboarding through your security stack.
Start the Hiring BriefA 30-minute call to map your ML estate, cloud footprint, compliance constraints, and the shape of the team needed: platform engineer, CI/CD lead, observability owner, or LLMOps specialist.
Three to five vetted MLOps engineers, ranked against the brief with prior work samples, platform certifications, and rate cards. No bait-and-switch profiles.
Technical interview on your terms, optional paid trial sprint, and reference checks. Replace any engineer at no extra cost inside the trial window.
Engineers onboard to your identity provider, repos, observability stack, and data perimeter. Delivery cadence locks to your sprint rhythm from week one.
MLOps economics shift by sector. The team scopes the platform to where the model count, regulatory load, or inference cost is already heaviest.
Risk-stratification models, imaging classifiers, and prior-auth scoring shipped under HIPAA audit trails, PHI redaction in features, and reviewer approvals.
Credit, fraud, and AML models with explainability captured per inference, challenger-model frameworks, and full lineage for model risk management committees.
Catalogue-velocity recommendation models, demand forecasting, and pricing engines with daily retraining and cold-start handling for new SKUs.
Vibration, vision, and telemetry models on plant-edge devices with central registry, over-the-air model rollout, and rollback on sensor drift.
ETA prediction, dynamic routing, and capacity planning models retrained on rolling weeks of telemetry, with shadow deployment for every model bump.
Recommendation and difficulty-tuning models shipped behind content-safety evaluation gates, bias monitoring, and learner-cohort drift alerts.
A working MLOps practice is a stack, not a single tool. Hire MLOps engineers fluent across orchestration, registry, serving, monitoring, and infrastructure layers.
Kubeflow Pipelines
Apache Airflow
MLflow
Weights & Biases
DVC
TorchServe
NVIDIA Triton
TF Serving
Prometheus
Grafana
AWS SageMaker
Vertex AI
Azure Machine Learning
Kubernetes (EKS, GKE, AKS)
TerraformThree models, one delivery floor. Switch between them as the build moves from platform stand-up to ongoing operations, without re-signing a master agreement.
The first sprint usually stands up a working pipeline. The next two harden the estate: registry, monitoring, retraining, and cost controls before traffic moves over.
Talk to Our TeamReal reviews from teams that have shipped with orangemantra. Verified on Clutch and GoodFirms.
"They built model governance the way auditors actually ask for it. Lineage, approvals, and rollback are no longer an afterthought."
Mar 2025
Feedback SummaryOrangemantra MLOps engineers built a model governance platform across credit, fraud, and claims models. The team handled MLflow registry, Vertex AI serving, drift dashboards, and a full audit trail inside the project window.
"They cut our retraining cycle from days to hours. Honest engineers who pushed back when our data freshness was the real bottleneck."
Sep 2025
Feedback SummaryA three-engineer pod stood up a Feast feature store, Airflow training pipelines, and KServe inference for a B2B SaaS recommendation product. Evaluation gates, canary rollout, and a rollback harness delivered as part of the engagement.
"Onboarded inside our VPC on day one. We never had to compromise on PHI handling to get models into production."
May 2025
Feedback SummaryOrangemantra delivered an MLOps platform for clinical imaging and risk-stratification models, including PHI-aware feature store, drift detection tuned to clinical risk, and reviewer approval workflows for every model promotion.
"They cut our per-call inference cost by a meaningful margin without breaking latency targets. Real engineering work, not vendor theatre."
Aug 2025
Feedback SummaryThe engagement built a multi-model serving layer, request batching, GPU sharing, and cost dashboards for a high-volume FinTech use case. Drift alerts and automated rollback delivered as part of the handover.
Independent recognition from industry bodies and analyst platforms. Listed only where verifiable.
CIO Choice Recognition
Top IT Service
WARC Award
Globus Certifications
NASSCOM
ISO 27001DevOps versions code. MLOps versions code, data, and models together, and adds continuous training, drift detection, model registries, and feature stores. The promotion gates are different too: an MLOps pipeline blocks deployment when an evaluation suite or fairness check fails, not just when a unit test fails.
An MLOps engineer owns the production lifecycle of ML models: CI/CD/CT pipelines, model registries, feature stores, inference infrastructure, monitoring for drift and degradation, retraining triggers, and the security and audit trail around all of it. The role focuses on keeping models reliable in production, not on training new ones.
A focused MLOps platform setup sits in the lower tens of thousands of dollars, an ongoing production engineering pod bills by sprint, and hourly rotations cover spike work. Orangemantra shares a fitted estimate after a scoping call.
Most engagements move from first call to billable work inside five to ten business days. Profiles arrive within 48 hours of the brief, interviews run on your schedule, and onboarding happens inside your VPC.
Yes. The same bench covers LLMOps work: prompt versioning, evaluation harnesses, vector store maintenance, token-cost monitoring, and inference routing across frontier APIs and self-hosted models. For deep LLM engineering, pair them with dedicated LLM developers.
AWS SageMaker, Google Vertex AI, and Azure Machine Learning are first-class. Kubernetes (EKS, GKE, AKS) for self-hosted estates. Terraform and Pulumi for infrastructure-as-code. Engineers pick based on your existing cloud footprint, not on tool preference.
Share your ML estate, cloud footprint, and the production reliability gap. Orangemantra returns a shortlist of vetted MLOps engineers within 48 hours, with rate cards and prior work samples attached.
Adjacent need? The same delivery floor supports AI development services and hire dedicated developers across data, ML, and full-stack roles.