Website:
ltm.com
Job details:
Greeting from LTM
We are thrilled to announce an exciting opportunity for the role of MLops Engineer. We are on the lookout for exceptional candidates who can join us within an immediate to 30 days' time. If you are passionate about this field and eager to take on new challenges, we would love to hear from you! This is a fantastic chance to become a part of our dynamic team and contribute to our projects with your expertise and skills.
About the Role
Experience:- 4 years to 15 years
Location:- Any LTM Office
Key Responsibilities
- End-to-End MLOps Lifecycle Management
- Design, implement and manage the full machine learning lifecycle using MLflow on Databricks
- Automate model training, validation, deployment and monitoring pipelines
- Model Deployment Serving
- Deploy ML models using Databricks Model Serving and integrate with AWS services e.g. S3, Lambda, SageMaker, EKS
- Ensure scalable and secure model serving with real-time and batch inference capabilities
- CICD for ML Pipelines
- Build and maintain CICD pipelines for ML workflows using tools like Bitbucket, Jenkins or Databricks Repos
- Implement automated testing, versioning and rollback strategies for models
- Monitoring Governance
- Set up monitoring for model performance drift detection and logging using MLflow and Databricks features
- Ensure compliance with data governance and model audit requirements
- Collaboration Documentation
- Work closely with data scientists, data engineers and DevOps teams to streamline ML operations
- Document architecture, workflows and best practices for reproducibility and scalability
- ML Model Development Support
- Assist in building and optimizing ML models using PySpark, scikit-learn, Pytorch, Keras within Databricks
- Provide guidance on feature engineering, hyperparameter tuning and model evaluation
- Cloud Infrastructure Management
- Manage and optimize cloud resources on AWS for ML workloads
- Implement cost-effective and secure infrastructure solutions for data and model storage
- GenAI Frameworks Good to Have
- Exposure to Generative AI frameworks like Databricks, MosaicML, LangChain or OpenAI APIs
- Ability to integrate GenAI models into enterprise workflows and applications
- Security Compliance
- Implement role-based access control (RBAC), encryption and secure data handling practices
- Ensure compliance with industry standards e.g. GDPR, HIPAA, SOC2
- Continuous Learning Innovation
- Stay updated with the latest trends in MLOps, cloud computing and GenAI
- Propose and implement innovative solutions to improve ML lifecycle efficiency
Mandatory Skills: MLOPS, MLOPS - Python
Required Skills
Preferred Skills
- Exposure to Generative AI frameworks
- Ability to integrate GenAI models into enterprise workflows
Click on Apply to know more.