Responsibilities –
· Design and implement cloud solutions, build MLOps on cloud (AWS or Google Cloud Platform)
· Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Flux, Kustomize, Circle CI, Airflow or similar tools
- Data science model containerization, deployment using docker, VLLM, Kubernetes
· Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
· Data science models testing, validation and tests automation
· Communicate with a team of data scientists, data engineers and architects, document the processes
· Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
Qualifications:
· 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.
· Good understanding of Apache SOLR.
· Proficient with Linux administration.
· Knowledge of ML models and LLM.
· Ability to understand tools used by data scientists and experience with software development and test automation
· Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS or Google Cloud Platform)
· Experience working with cloud computing and database systems
· Experience building custom integrations between cloud-based systems using APIs
· Experience developing and maintaining ML systems built with open-source tools
· Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
· Experience developing containers and Kubernetes in cloud computing environments
· Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
· Ability to translate business needs to technical requirements
· Strong understanding of software testing, benchmarking, and continuous integration
· Exposure to machine learning methodology and best practices
· Good communication skills and ability to work in a team
Technical skills:
Skill Area | Includes | Weight (%) |
Platform Reliability & Containerization | Kubernetes, Docker, Microservices, Linux | 30% |
MLOps & AWS Cloud | Model deployment, versioning, monitoring, AWS (SageMaker, S3, Lambda, EKS) | 25% |
CI/CD & GitOps | GitHub Actions, Flux | 15% |
Monitoring & Observability | Splunk, Grafana, Prometheus, performance tracking | 15% |
Integration & Collaboration | Python scripting, MongoDB, API integrations, Apache Solr, LLM awareness, teamwork with data scientists & engineers | 15% |
Never miss an opportunity! Create an alert based on the job you applied for.
