MLOps Engineer (Remote)
Joveo Ai · India
- Hybrid
- Full-time
- $120,000 / year
- India
Job highlights
- Build and operate ML model production infrastructure.
- Ensure reliable, repeatable model deployments.
- Automate ML pipelines and workflows.
- Monitor model performance and quality.
- Integrate ML systems with CI/CD and cloud.
About the role
About Joveo
Every company says they're "AI-first." We actually are. Joveo's recruitment advertising platform processes millions of hiring decisions through machine learning, real-time bidding, and predictive analytics — helping the world's largest employers find the right people, faster and fairer. But we're not done. Not even close.
Role Overview: MLOps Engineer
We are hiring an MLOps Engineer to build and operate the infrastructure that takes Joveo's machine learning models from experiment to production reliably and repeatedly. You will be the backbone of our AI delivery pipeline — ensuring models are versioned, monitored, and deployed with the same rigor as our software systems.
Key Responsibilities:
- Build and maintain ML pipelines from training to deployment and monitoring
- Design model registries, experiment tracking, and feature store infrastructure
- Automate model retraining, validation, and deployment workflows
- Monitor deployed models for performance drift, data skew, and prediction quality
- Collaborate with ML engineers and data scientists to streamline the path to production
- Integrate ML infrastructure with CI/CD systems and cloud platforms
Required Skills & Qualifications:
- Strong experience with ML platforms — MLflow, Kubeflow, SageMaker, or Vertex AI
- Proficiency in Python and infrastructure tooling (Docker, Kubernetes, Terraform)
- Experience building automated model training and deployment pipelines
- Familiarity with feature stores (Feast, Tecton, or Hopsworks)
- Understanding of model monitoring tools and drift detection approaches
- Strong DevOps/platform engineering fundamentals applied to the ML lifecycle
Equal Opportunity Employer:
Joveo is an equal opportunity employer. We are committed to building an inclusive workplace and welcome applications from all qualified individuals regardless of race, color, ethnicity, nationality, gender, gender identity or expression, sexual orientation, age, religion, disability, marital status, or any other characteristic protected by applicable law. All hiring decisions are made solely on the basis of qualifications, skills, and demonstrated ability.
If your dream job is one that doesn’t fit neatly into a job title — apply. Joveo. Where AI meets the future of work.
Key skills/competency
- MLOps Engineer
- Machine Learning
- Python
- Docker
- Kubernetes
- Terraform
- CI/CD
- Model Deployment
- Feature Stores
- Model Monitoring
Skills & topics
- MLOps Engineer
- Machine Learning
- Python
- Docker
- Kubernetes
- Terraform
- CI/CD
- MLflow
- Kubeflow
- SageMaker
- Vertex AI
- Feature Store
- Model Monitoring
- DevOps
- AI
- Data Science
- Cloud Platforms
- Remote
How to get hired
- Tailor your resume: Highlight MLOps, Python, Docker, Kubernetes, and ML platform experience.
- Showcase ML pipelines: Detail your experience building and automating ML training and deployment workflows.
- Demonstrate cloud proficiency: Emphasize your skills with Terraform and cloud platforms like AWS, GCP, or Azure.
- Prepare for technical questions: Be ready to discuss ML monitoring, drift detection, and feature stores.
- Research Joveo: Understand their AI-first approach and impact on recruitment advertising.
Technical preparation
Behavioral questions
Frequently asked questions
- What are the key MLOps platforms used at Joveo for this role?
- For this MLOps Engineer role at Joveo, experience with ML platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI is highly valued. Proficiency in these tools will be crucial for building and operating the ML infrastructure.
- What programming languages and infrastructure tools are essential for the MLOps Engineer position at Joveo?
- The MLOps Engineer position at Joveo requires strong proficiency in Python for scripting and automation, along with essential infrastructure tooling like Docker for containerization and Kubernetes for orchestration. Experience with Terraform for infrastructure as code is also a key requirement.
- How does Joveo ensure ML models are monitored in production?
- Joveo's MLOps Engineer is responsible for monitoring deployed models for performance drift, data skew, and prediction quality. This involves implementing and utilizing appropriate model monitoring tools and drift detection approaches to ensure models remain effective in production.
- What is the role of a Feature Store in the MLOps Engineer position at Joveo?
- Familiarity with feature stores is important for the MLOps Engineer at Joveo. Feature stores like Feast, Tecton, or Hopsworks are used to manage and serve features for ML models, playing a key role in streamlining the path to production.
- How are ML models integrated into Joveo's existing systems?
- The MLOps Engineer at Joveo will integrate ML infrastructure with CI/CD systems and cloud platforms. This ensures that the machine learning lifecycle is managed with the same rigor as software systems, enabling reliable and repeatable deployments.
- Is this an MLOps Engineer role with hands-on development or more infrastructure focused?
- This MLOps Engineer role at Joveo involves both building and operating ML infrastructure and automating workflows. You'll be responsible for the backbone of the AI delivery pipeline, requiring a blend of hands-on development and strong DevOps/platform engineering skills applied to the ML lifecycle.