Senior AI Systems Engineer

Uster Technologies; Coimbatore District; On-site; INR 400,000 - 700,000; Full time

Job summary

Uster Technologies is seeking a Senior AI Systems Engineer to lead verification, validation, and production deployment of AI solutions across cloud, edge, and on‑premise environments.

You will own end-to-end system integration, define V&V strategies, and mentor teams in testing and quality assurance to ensure reliability and scalability of industrial AI products.

Qualifications

• Master's or Bachelor's in CS/Engineering with AI focus.
• 10+ years in software or AI systems engineering.
• Proven track record delivering production AI solutions.

Responsibilities

• Design and maintain production-grade AI software components.
• Lead AI solutions deployment across cloud, edge, and on‑premise.
• Own end-to-end system integration of software, AI, hardware.
• Define and improve V&V strategy for AI.
• Develop automated test frameworks and quality gates.
• Collaborate with data scientists to productionize prototypes.
• Establish deployment, monitoring, and observability processes.

Skills

Python

Software architecture

AI/ML deployment

CI/CD pipelines

Testing frameworks

Root-cause analysis

Distributed systems

Performance benchmarking

Systems integration

Debugging skills

Observability

Education

Master's or Bachelor's in CS/Engineering

Tools

Docker

Kubernetes

CI/CD automation

Infrastructure as Code

ML deployment frameworks

Model monitoring

Job description

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

Selection process:

1st round: Online technical assessment

2nd round: Interview via Teams

3rd round: HR discussion