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Execute comprehensive testing of AI datacenter networking solutions, focusing on SONiC-based systems, smart NIC integration, and high-performance networking for AI workloads.
Key Responsibilities:
Design large scale testing topologies with GPU/Traffic Generators and execute test cases for SONiC/network operating system functionality.
Validate smart NIC performance and integration with SONiC datapath
Perform functional, performance, aour company nd stress testing of AI networking scenarios
Develop automated test scripts for regression testing and continuous validation
Execute interoperability testing between different hardware vendors and software versions
Validate RDMA, high-speed Ethernet performance for AI workloads
Test failover scenarios and network resilience under various failure conditions
Document test results, file detailed bug reports, and track issue resolution
Collaborate with development teams to reproduce and isolate defects
Participate in customer proof-of-concept testing and field issue reproduction.

We are seeking a Senior Automation Engineer to join our Big Data team, where we build cutting-edge, AI-driven applications that empower the worlds largest financial institutions in the fight against financial crime.
Youll be part of a dynamic group responsible for the analytics and developer experience foundations, enabling software developers, data engineers, and data scientists to deliver scalable, high-quality analytics solutions quickly. In this role, youll ensure the quality, reliability, and performance of our intelligent cloud-based products through robust automated testing.
How will you make an impact?
Design, develop, and maintain automated tests for backend systems and data pipelines.
Implement and manage a scalable testing framework to support continuous integration and delivery.
Create test plans, and maintain detailed documentation of testing processes, results, and quality metrics.
Collaborate with developers and product managers to understand system architecture and business requirements.
Participate in requirements analysis, technical reviews, and test planning.
Identify, document, and track defects, drive resolution with development teams.
Monitor production environments, investigate anomalies, and push for timely fixes.
Ensure data health and correctness across systems.
Continuously improve QA processes, tools, and best practices.
Advocate for quality across the development lifecycle.

We are looking for a highly skilled Senior Developer - Python Automation Infrastructure and testing to join our R&D Integration Automation team. This role is focused on designing, developing, and maintaining advanced automation solutions across unit, integration, system, and end-to-end testing levels.
The ideal candidate is a hands-on developer with strong expertise in Python and Pytest, experienced in building and executing automated tests, and comfortable working with modern CI/CD pipelines and automation tools. The Senior Developer will act as a technical mentor and key contributor, ensuring best practices are followed and test automation is seamlessly embedded into the development lifecycle. Knowledge of RF systems is a strong plus.
Key Responsibilities:
Design, implement, and maintain automation frameworks and test scripts using Python and Pytest.
Write unit tests, integration tests, and regression suites to ensure comprehensive test coverage.
Develop and execute system-level and end-to-end automation, including API and UI layers (Selenium preferred).
Contribute to and optimize CI/CD pipelines (Jenkins, Docker, Git) for reliable and scalable test automation.
Collaborate closely with SW developers, RF, QA, and DevOps engineers to embed automated testing into every phase of the product lifecycle.
Perform code reviews, provide technical guidance, and ensure automation code meets high quality and maintainability standards.
Analyze test results, identify root causes of failures, and drive timely resolution in partnership with development teams.
Continuously evaluate and introduce new tools, libraries, and practices to improve automation efficiency.
Support integration activities and ensure smooth interaction between hardware, software, and automation systems.

We are looking for a Junior Validation Engineer to test the Machine Learning systems and the logics encapsulating them.
What will you do?
Reach validation of the Computer Vision algorithms and evaluate their performance under different environmental conditions.
Write test plans.
Create automation tools.
Perform manual testing and compare versions visually (side-by-side screenshots).

As a Platform QA Engineer, you'll play a crucial role in ensuring the performance, reliability, and scalability of our workflow engine infrastructure. You'll focus on validating environments, functionality, authentication systems, and ensuring proper coverage for our microservices architecture. You'll work closely with development teams to validate that our platform components meet design specifications and performance requirements.
Key Responsibilities:
Design and execute plans for REST/gRPC APIs and workflow engine functionality
Validate authentication and authorization flows across microservices
dynamic task addition and execution in the workflow engine
Implement automated suites for contract validation
Verify proper service communication in environments
Perform integration testing between workflow components and services
Create comprehensive coverage for critical platform functionality
Collaborate with developers to improve testability of platform services
Identify, document, and track issues, validate they're resolved on time.

In one sentence
Design and implement a modern software development lifecycle and CI/CD operating model augmented with agentic AI. Partner with engineering teams to assess current practices, identify pain points, introduce spec-driven and context-engineered workflows, and deliver practical tooling and PoCs that can be adopted at scale.
What will your job look like?
Assess existing SDLC, CI/CD, and operational practices; identify gaps, bottlenecks, and improvement opportunities.
Evaluate current AI-for-engineering assets and developer enablement maturity (assistants, agents, templates, guardrails).
Select, pilot, and operationalize AI tools for developers, CI/CD automation, and post-delivery operations.
Build PoC/MVP agentic flows and tools; define adoption patterns and hand over mature solutions to delivery units.
Participate as a thought leader in architecture and SDLC forums; define standards, patterns, and governance where needed.
Mentor teams and uplift engineering capability in AI-driven development, delivery, and operational readiness.