Senior ML Engineer (MSc. or PhD) contributing to AI training projects involving model development, training and inference systems, numerical computing, performance optimization, and Python.
Compensation
Pay: $60–$120/hour
Responsibilities
Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
Implement model components, data pipelines, evaluation systems, and numerical methods.
Build reproducible programmatic workflows using Python and command-line tools.
Requirements
Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
Lead technical advisor and engineer for Mist WAN and SD-WAN solutions, designing, deploying, and troubleshooting enterprise network environments.
Responsibilities
Serve as a technical advisor to the customer
Design, deploy, configure, operate, and troubleshoot Mist-managed WAN Edge and SSR-based SD-WAN solutions
Provide technical expertise for WAN Edge configuration, routing, application policies, traffic steering, path selection, high availability, and site connectivity
Support Mist Wireless environments, including wireless configuration, operations, monitoring, troubleshooting, and optimization
Use Marvis Virtual Network Assistant, Service Level Expectations (SLEs), Mist insights, events, alerts, and telemetry to identify and troubleshoot network, end-user connectivity, and performance issues
Lead backend engineering for user incentive and financial benefit modules, integrating AI capabilities and ensuring robust, secure, and scalable systems.
Responsibilities
Design and implement solutions for user incentives, experience improvements, and business automation
Drive requirements analysis, technical design, development, testing, production rollout, ongoing operation, and post-launch evaluation
Develop and maintain user incentive and financial benefit modules
Strengthen rule execution, monetary calculations, disbursement, state transitions, idempotency, consistency, failure compensation, reconciliation, and auditing
Contribute to engineering and productionization of AI applications and AI-powered user experiences
Requirements
Bachelor's degree in Computer Science or related field
Design, build, test, deploy, and support scalable data pipelines and data products on Azure Databricks.
Compensation
Exempt roles are also performance bonus eligible
Responsibilities
Design, build, test, deploy, and support scalable data pipelines and data products on Azure Databricks.
Develop ingestion and transformation processes for data from on-premises, cloud, SaaS, and third-party sources using batch and, where appropriate, streaming patterns.
Use SQL and Python or PySpark to transform, validate, and optimize large and complex datasets.
Design and maintain reusable data structures across raw, refined, and curated layers, applying dimensional modeling and star schema principles where appropriate.
Build reliable Delta Lake solutions and apply Databricks capabilities for orchestration, performance, observability, and data quality.
Requirements
Bachelor’s degree in technology, engineering, computer science, or a related field, or an equivalent combination of education and experience.
Design, develop and maintain data solutions for data generation, collection, and processing, creating efficient data pipelines and ensuring data quality across systems.
Develop custom software solutions to design, code, and enhance components across systems or applications using modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Responsibilities
Expected to perform independently and become an SME
Required active participation/contribution in team discussions
Contribute in providing solutions to work related problems
Collaborate with cross-functional teams to understand project requirements and translate them into effective software solutions
Maintain and enhance existing software components to improve performance and reliability
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