ATMECS Technologies Pvt., Ltd.
Location
Secunderabad | India
Job description
Role: DevOps(MLOps)
Experience: 5-8 Years
Location: Hyderabad
Job Description:
JobOverview
Role: Machine Learning Engineer
Atmecs Technologies, is seeking Machine Learning Engineer to join the AI ECG team. This team builds enterprise level scalable and sustainabledataand model pipelines to serve the analytic needs of business impacting problem statements. In this role, you are a critical member of thedatascienceteam focused to operationalize the ML and AI models, entails model management and monitoring too. You will be recommending innovative ways to automate the MLOps pipelines on Azure or other platform and set standards that would ensure repeated success.
This capability is leveraged to fuel advanced Analytical solutions, Machine Learning and Deep Learning. It is also responsible for implementing and enhancing community of practice to determine the best practices, standards, and MLOps frameworks to efficiently delivery enterpriseAIsolutions.
This role works in close collaboration withDataScientists,DataEngineers, Platform Engineers and Tech Expertise to support the analytic consumption needs. Enhances the performance of the models and automates the production pipelines to gain efficiency.
Role Responsibilities
Establish and Implement MLOps practices:
- Development of end-to-end MLOps framework and Machine Learning Pipeline using GCP/Azure/AWS, Vertex AI or any other Software tools as per the client's requirement
- Management ofdatapipelines including config, ingestion and transformation from multipledatasource like Big Query, Azure & Google cloud storage etc
- Build and set-upDatapipelines, ML CI/CD pipelines
- Re-Training and Monitoring Pipeline setup with multiple criteria
- Serving Pipeline with multiple creation
- Resource and Infra Monitoring configuration and pipeline development using GCP service.
- Automated pipeline Development for Continuous Integration (CI)/Continuous Deployment (CD) Continuous Monitoring (CM)/Continuous Training (CT)
- Branching strategies and Version Control using GitHub
- ML Pipeline orchestration and configuration using Kubeflow/Airflow/MLflow.
- DAG and Workflow orchestration using airflow/cloud composer.
- Code refactorization & coding best practices implementation as per industry standard
- Technology-Stack suggestion based on 360 Deg Analysis.
- Implementing MLOps practices on project and follow the set MLOps practices.
- Support the ML models throughout the E2E MLOps lifecycle from development to maintenance.
Architecture:
- The solution may be built over Monolithic or Micro Services Architecture
- Agile software Development concept
- Architecture Design for HLD, LLD and Solution design
Team Mentoring:
- Programming language Pattern Design implementation
- Review projects and suggestion for improvement
- Knowledge sharing session with team for specific ML Ops topics.
- Guide/Mentor team members for MLOps framework development.
Research, Evolve and Publish best practices:
- Research and operationalize technology and processes necessary to scale ML Ops
- Ability to research and recommend MLOps best practices on new technologies, platforms, and services.
- MLOps pipeline improvement plan and suggestion
Communication and Collaboration:
- Collaborate with technical teams likeDataScienceLead,DataScientist,DataEngineer and Platform owner.
- Knowledge sharing with the broader analytics team and stakeholders is essential.
- Communicate on the on-goings to embrace the remote and cross-geography culture.
- Align on the key priorities and focus areas.
- Ability to communicate the accomplishments, failures, and risks in timely manner.
Embrace learning mindset:
. Continually invest in your own knowledge and skillset through formal training, reading, and attending conferences and meetup.
Documentation :
- Document MLOps Process, Development, Architecture & Innovation etc and be instrumental in reviewing the same for other team members.
Must - have technical skills and experience
- Minimum qualification- Bachelor's degree (Full Time)
- Total Experience required more than 5 Years
- Expertise and at least 3 Years of professional experience in MLOps E2E framework
- Expertise inDataTransformation and Manipulation through Big-Query/SQL
- Professional experience Vertex AI and GCP Services
- Expertise in Python
- Airflow/Cloud composer Experience
- Kubernetes/Kubeflow Experience
- MLflow Professional experience
- TFX Professional experience
- Docker -container Experience
- At least 4yrs of professional experience in the related field ofDataScience
- Strong communication skills both verbal and written including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
- Passionate about agile software processes,data-driven development, reliability, and systematic experimental.
Good To Have Skill
- Azure/GCP certification
- Understanding of Shipping/Logistics industry
- AutoML Concept
- Machine Learning -Concept of Algorithms
- Deep Learning- Concept of Algorithms
- Time Series Analysis- Concept of Algorithms
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