MLOps; and DevOps Sr Lead​/Architect

Job Title :

- MLOps (and Dev Ops) Sr Lead / Architect

Employment Type

:

- W2

Duration :

- Long Term

Visa Type :

- All Visa applicable which are ready for W2

Location
- Bellevue, WA (Day-1 Onsite)

Industry :

- Telecom

Need a strong candidate who has experience with MLOps (and Dev Ops) architecture and implementation experience and preferably understands Full stack development and was working as a full stack engineer in his previous roles.

• Familiarity with ML lifecycle tools (MLflow, DVC, Airflow) and version control for data and models.
• Experience with data science and ML libraries:
  Tensor Flow, PyTorch, Scikit-Learn, etc.
• Knowledge of container orchestration with Kubernetes and model serving frameworks (e.g., Seldon, Tensor Flow Serving).

• Proficient in CI/CD tools such as Jenkins, Git Lab CI, or Circle
  
  CI.
• Cloud platforms expertise (AWS, Azure, Google Cloud) and cloud-native services (Docker, Kubernetes).
• Experience with IaC (Terraform, Cloud Formation) and configuration management tools (Ansible, Chef).

• JavaScript/Type Script, HTML, CSS, and frameworks like React, Angular, or Vue.js.
• Experience with UI/UX design principles and responsive web design.
• Proficiency in Node.js, Python, or similar back-end technologies.
• Knowledge of RESTful APIs, Graph
  
  QL, and microservices architecture.

• Experience in Telco domain will be a huge plus but not mandatory
• Experience with data engineering tools (e.g., Apache Spark, Kafka).
• Knowledge of distributed computing and big data solutions.
• Prior experience with A/B testing, monitoring model performance, and tracking business metrics related to ML.

Need a strong candidate who have experience with MLOps (and Dev Ops) architecture and implementation experience and preferably understands Full stack development and was working as a full stack engineer in his previous life.

• Implement CI/CD pipelines specifically tailored for machine learning model deployment.
• Design and develop MLOps workflows, including model training, validation, deployment, monitoring, and retraining.
• Collaborate with data scientists to containerize ML models and ensure efficient deployment to production.
• Utilize ML monitoring tools and frameworks (e.g., MLflow, Kubeflow, Airflow) to monitor model drift, performance, and data quality in production environments.
• Manage model versioning, artifact tracking, and reproducibility of experiments.

• Implement and manage CI/CD pipelines to automate code deployment and delivery.
• Design infrastructure as code (IaC) using tools like Terraform, Cloud Formation, or Ansible.
• Deploy and manage cloud resources in AWS, Azure, or Google Cloud.
• Monitor system performance and optimize applications for reliability and performance.

• Has experience Designing and implementing scalable and efficient front-end and back-end solutions.
• Build user-friendly web interfaces and responsive applications using frameworks like React, Angular, or Vue.js.
• Develop back-end APIs and services using Node.js, Django, or similar technologies.
• Ensure optimized performance, security, and scalability of web applications.