Senior AI Systems Engineer

Senior AI Systems Engineer (Verification & Validation)

Must Have

Education: Master or Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Mechatronics Engineering, Artificial Intelligence, or a related field.

Experience:

• 10+ years of experience in software engineering, systems engineering, AI engineering, or a related field
• Proven experience delivering complex software, AI, or system solutions from concept through industrialization and long-term maintenance
• Experience integrating AI/ML solutions into production environments across cloud, edge, and on-premise systems
• Experience with system integration, software architecture, distributed systems, and production-grade software development
• Experience defining and executing Verification & Validation (V&V) activities, including test strategy, qualification, acceptance testing, and release readiness
• Experience with automated testing, regression testing, CI/CD pipelines, quality assurance processes, and software release management
• Experience investigating complex system failures, performing root-cause analysis, and driving corrective actions
• Experience working in multidisciplinary environments involving software, AI, hardware, and product teams

Skills:

• Strong software engineering skills, particularly in Python
• Strong understanding of software architecture, APIs, distributed systems, and system integration
• Solid understanding of machine learning workflows and production deployment of AI solutions
• Experience establishing testing frameworks, quality gates, traceability, and release processes
• Strong debugging, root-cause analysis, troubleshooting, and problem-solving skills
• Familiarity with performance benchmarking, reliability engineering, scalability, and system monitoring
• Ability to define and maintain engineering processes that ensure quality, reproducibility, and maintainability

Communication:

• Ability to collaborate effectively with data scientists, software engineers, QA teams, product teams, and external partners
• Ability to communicate technical risks, quality concerns, test results, and architectural decisions clearly to both technical and non-technical stakeholders

Nice to Have

Infrastructure & Deployment

• Experience working with Linux-based systems, containers (Docker), deployment technologies, and cloud-native environments
• Experience with CI/CD automation, Infrastructure as Code, and operational monitoring
• Exposure to MLOps practices, model lifecycle management, model monitoring, and ML deployment frameworks

Embedded & Edge Systems

• Experience with embedded, edge, or real-time systems
• Experience working with hardware accelerators such as NVIDIA Jetson, GPU-based systems, or similar platforms

Industrial AI

• Experience in computer vision systems and image-processing applications
• Exposure to industrial, manufacturing, inspection, or automation environments
• Experience supporting production deployments and customer acceptance testing

Preferred attributes

• Quality-focused: Drives engineering excellence through verification, validation, test automation, and continuous improvement
• Hands-on: Comfortable working across software, systems, testing, deployment, and operational activities
• Pragmatic: Balances speed of delivery with robustness, maintainability, and product quality
• Ownership mindset: Takes responsibility for delivering reliable, scalable, and supportable solutions
• Systems thinker: Understands interactions between data, models, software, infrastructure, hardware, and customer environments
• Analytical: Applies structured troubleshooting and root-cause analysis to complex technical problems
• Adaptable: Comfortable working in evolving environments and supporting multiple concurrent initiatives
• Collaborative: Works effectively across international teams and cross-functional disciplines
• Customer-oriented: Understands the importance of solution reliability, acceptance criteria, and operational readiness

Your tasks

• Design, develop, and maintain production-grade AI software components, services, and system integrations
• Lead the integration of AI solutions into cloud, edge, and on-premise environments
• Own end-to-end system integration activities across software, AI, infrastructure, and hardware components
• Define, maintain, and continuously improve the Verification & Validation (V&V) strategy for AI solutions
• Develop and maintain automated test frameworks, regression test suites, qualification procedures, and quality gates
• Execute system-level testing, performance benchmarking, reliability assessments, and release qualification activities
• Ensure traceability, reproducibility, verification evidence, and release compliance across AI solution lifecycles
• Drive reliability, maintainability, scalability, and operational readiness improvements across AI solutions
• Investigate field issues and perform structured root-cause analysis of software, system, and deployment failures
• Support customer pilots, factory acceptance tests, site acceptance tests, and production deployments
• Collaborate closely with data scientists to transition prototypes into robust, maintainable production solutions
• Contribute to technical architecture decisions, engineering standards, and software quality best practices
• Establish deployment, monitoring, observability, and operational readiness processes for AI products
• Mentor engineering teams on testing, validation, quality assurance, and systems engineering practices

Interested in shaping the future of textile quality assurance?
Apply now and join our mission to deliver excellence in every thread.

Selection process:

1st round: Online technical assessment

2nd round: Interview via Teams

3rd round: HR discussion