Hire Top MLOps Engineers in India!
Years of experience
Customer satisfaction
What Makes Benchkart a Great Partner for Hiring MLOps Engineers?
Benchkart blends AI-driven talent intelligence, a deep vendor ecosystem, and Avance Group’s governance to deliver high-performance MLOps with unmatched reliability.
Security-First Delivery
Governed pipelines, secure data flows, compliance-ready deployments, and enterprise access controls.
Proven Talent Network
MLOps engineers sourced from our bench, passive DevOps/AI engineering networks, and 2,000+ vetted partners.
Growth-Driven Engineering
Engineers skilled in scalable ML systems, CI/CD for AI, feature stores, observability, and automated retraining.
Cutting-Edge Tech Stack
Developers bring hands-on expertise in:
Expertise in MLflow
Kubeflow
Airflow
SageMaker
Vertex AI
Azure ML
Docker
Kubernetes
Terraform
Databricks
Feast
Ray, and advanced LLMOps frameworks
Services We Offer
End-to-End MLOps Pipeline Development
Model Deployment & Serving Architecture
Feature Engineering & Feature Store Management
LLMOps & GenAI Infrastructure
ML Monitoring & Drift Detection
Cloud ML Platform Engineering
Expertise of Our MLOps Engineers
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Automated training pipelines
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Model validation gates
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GitOps workflows
Roles & Responsibilities: MLOps Engineers build automated ML pipelines, enforce testing/validation steps, integrate version control, establish reproducible workflows, and ensure ML releases follow enterprise CI/CD standards.
Model APIs
Docker/Kubernetes
A/B, canary & blue-green deployments
Roles & Responsibilities: They deploy models as scalable services, optimize inference throughput, manage container orchestration, implement rollout strategies, and maintain high availability for production models.
MLflow / Weights & Biases
Model registries
Reproducibility frameworks
Roles & Responsibilities: Engineers track experiments, manage model lineage, maintain registries, ensure reproducibility, and coordinate model progression from experimentation to production.
Model performance tracking
Telemetry pipelines
Automated retraining triggers
Roles & Responsibilities: They monitor prediction quality, detect model/data drift, integrate alerting systems, develop dashboards, and design retraining workflows that keep models reliable over time.
AWS Sagemaker
Azure ML
GCP Vertex AI
Roles & Responsibilities: They architect ML systems on cloud platforms, optimize compute usage, configure training clusters, manage scalable inference endpoints, and integrate storage, networking, and security layers.
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Feast, Hopsworks, Databricks Feature Store
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Real-time feature pipelines
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Feature versioning & governance
Roles & Responsibilities: They design and manage feature stores, collaborate with data engineering teams, enforce consistent features across training and inference, and ensure metadata-driven governance.
How We Hire Developers
With a structured multi-stage hiring process, we onboard only high-calibre MLOps Engineers.
Skill Benchmarking
Thorough CV & background evaluation
Human Vetting
Interview with HR specialist
Experience Validation
Communication & soft-skills assessment
Cultural Fit
Technical interview with Senior MLOps Architect
Hire Developers from Benchkart
STEP 1 Inquiry
We understand your ML architecture, pipelines, cloud stack, security requirements, and operational objectives.
STEP 2 Developer Selection
AI-matched MLOps engineers curated from our bench, passive specialists, and vetted partner ecosystem.
STEP 3 Integration
Engineers join your ML teams, data pipelines, cloud environment, and sprint cycles with minimal ramp-up.
STEP 4 Scaling
Scale your MLOps capability as your ML operations mature, backed by governed delivery and continuity.
Choose the Right Development Model for Your Business
Flexible engagement models designed for ML, AI, and production operations.
MLOps Team Augmentation
Add MLOps engineers quickly to expand automation and model deployment bandwidth.
Dedicated MLOps Squad
A full-time team aligned exclusively to your ML platform, pipelines, and operational governance.
Full MLOps Outsourcing
We manage pipeline design → deployment → monitoring → retraining → governance end-to-end.
Top Reasons to Choose Benchkart for MLOps Engineer Hiring
Quality + speed + governance built for enterprise delivery.
Built on Avance Group’s Talent Engine
AI-powered skill matching supported by enterprise governance and delivery excellence.
Unmatched Speed 48-Hour Shortlists
Receive curated MLOps Engineer profiles within 48–72 hours.
Massive Vendor Ecosystem 2000+ Strong
Access India’s most robust network of MLOps, ML engineering, DevOps, and AI delivery partners.
Wisestep ATS + CRM Skill-First Precision
AI evaluates candidates on MLOps pipeline maturity, cloud ML expertise, deployment experience, and domain readiness.
Bench-Ready MLOps Engineers
Professionals skilled in MLflow, Kubeflow, Airflow, containerization, cloud ML platforms, feature stores, LLMOps, and monitoring.
Governed Delivery with Enterprise SLAs
Observability, drift governance, cost optimization, retraining automation, and continuity frameworks.
Backed by Avance Group Global Trust
Operating across 14+ countries with a strong track record in ML and AI engineering.
Need a Dedicated MLOps Engineering Team?
Hire pre-vetted MLOps Engineers who deliver scalable, automated, and production-grade ML systems from day one.
Shortlist in 48 hours. Onboarding in 5–10 days.
Operates across
Industries We Support for MLOps Engineer Hiring
Benchkart supports ML and AI modernization across global industries.
BFSI & FinTech
Healthcare
E-commerce
Manufacturing
SaaS
Logistics
Telecom
Hospitality
FAQs
1. What does an MLOps Engineer do?
They build automated pipelines, deploy ML models, monitor performance, manage retraining workflows, and maintain production AI systems.
2. How is MLOps different from DevOps?
MLOps adds model lifecycle management, drift detection, feature stores, and ML-specific validation steps on top of standard DevOps practices.
3. Which tools do MLOps Engineers use?
Kubeflow, MLflow, Airflow, Azure ML, Vertex AI, SageMaker, Docker, Kubernetes, Terraform, Feast, and monitoring platforms.
4. Do MLOps Engineers work on LLMOps?
Yes, prompt/versioning governance, LLM evaluation frameworks, embedding pipelines, and scalable LLM deployments are common tasks.
5. What does it cost to hire an MLOps Engineer in India?
Typically $30–$75 per hour, depending on cloud expertise, automation depth, and deployment scale.
6. Do they collaborate with ML engineers and data engineers?
Absolutely, they sit at the intersection of data, ML, and DevOps teams.
7. Can Benchkart provide MLOps Engineers within 48 hours?
Yes, curated shortlists are usually delivered within 48–72 hours.
8. Can I hire a dedicated MLOps team?
Yes, including pipeline engineers, MLOps specialists, cloud engineers, and AI platform architects.
9. Do they ensure model reliability & drift control?
Yes, through monitoring, automated alerts, retraining frameworks, and governance.
10. Why hire from Benchkart?
You gain highly vetted MLOps talent backed by governance, vendor oversight, and enterprise SLAs.
Get in Touch with Benchkart Reliable Tech Talent Delivery
We’re happy to answer any questions you may have and help you understand how Benchkart can support your technology hiring and delivery needs.
Your benefits:
- Vendor-verified
- Delivery-focused
- AI-driven
- Results-oriented
- Execution-ready
- Transparent
What happens next?
We schedule a quick call at your convenience
We understand your role, timeline, and delivery context
We activate the right talent path