New opportunity

Senior Engineer (Robotics & AI)

American Bureau of Shipping · Singapore

About this role

The AI/Robotics Engineer will own the end-to-end delivery of advanced AI and autonomous inspection solutions for the maritime and offshore industries, covering client problem definition through to production deployment and adoption. This is a combined role encompassing development, delivery, and advisory functions, requiring collaboration with cross-functional teams and serving as the primary technical point of contact for internal and external stakeholders, holding joint accountability for project scope, schedule, and quality.

Key Responsibilities:

**Project Delivery & Participation:** Accountable for the end-to-end delivery of AI and robotics projects, managing scope, milestones, resourcing, risk, and acceptance criteria. This includes identifying business/operational problems and translating them into structured technical workstreams with clear success metrics, while participating in project management activities like backlog prioritization and tracking project health.
**Client Engagement & Advisory:** Act as a trusted technical advisor on AI, computer vision, and autonomous inspection, providing feasibility assessments and solution options. Responsibilities include running discovery sessions, requirements workshops, and supporting business development with technical scoping and proof-of-concept demonstrations.
**Technical Direction & Governance:** Provide technical input for perception and AI workstreams, specifically involving sensor fusion (camera + LiDAR), SLAM, defect/anomaly detection, and image recognition for structural inspection. The role requires reviewing architecture and code, validating model performance, and ensuring solutions are production-ready on Microsoft Azure or equivalent, while adhering to MLOps, CI/CD, security, and data governance standards.

Required Qualifications & Experience:

The role demands sufficient hands-on depth in robotics perception, computer vision, and cloud AI to direct technical decisions and communicate credibly with clients and engineering leadership. The position involves aligning engineering, data, product, operations, and class/technical functions around a common delivery plan, and managing vendor/partner collaborations.

About ABS:

ABS is a global leader in marine and offshore classification, dedicated to promoting the security of life and property at sea. The company supports organizations navigating the evolving seascape of regulations and new technologies, focusing on safety, reliability, and efficiency.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Robotics perceptionComputer visionCloud AISensor fusionLiDARSLAMDefect and anomaly detectionImage recognitionMicrosoft AzureMLOpsCI/CDData governance

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End Solution Delivery
Owning the complete lifecycle of advanced AI and autonomous inspection solutions, from initial client problem definition through to final production deployment and adoption.
Technical Advisory and Client Trust
Serving as the primary technical advisor on AI, computer vision, and autonomous inspection, which includes conducting feasibility assessments, running requirements workshops, and supporting business development with technical scoping.
Production-Ready Technical Governance
Ensuring solutions are production-ready on Microsoft Azure or equivalent while strictly adhering to MLOps, CI/CD, security, and data governance standards, alongside reviewing architecture and code.
Cross-Functional Alignment and Project Accountability
Holding joint accountability for project scope, schedule, and quality by aligning diverse functions (engineering, data, product, operations, class/technical) around a common delivery plan.

Likely challenges to think through

Balancing Technical Depth with Business Delivery
The role requires sufficient hands-on depth in robotics perception and cloud AI to direct technical decisions, but it is also a delivery and advisory role. Preparation should focus on articulating complex technical trade-offs (e.g., model accuracy vs. deployment latency) in terms of business impact for non-technical stakeholders.
Translating Operational Problems to Technical Workstreams
The requirement to identify vague business/operational problems and translate them into structured technical workstreams with clear success metrics presents a translation challenge. Preparation should involve case studies demonstrating how you have successfully scoped ambiguous real-world problems into measurable engineering deliverables.
Managing Diverse Stakeholder Expectations
The role involves aligning engineering, data, product, operations, and class/technical functions, alongside external clients and vendors. The difficulty lies in managing conflicting priorities across these groups. Preparation should involve examples of successfully mediating technical disagreements between different functional departments.

Practice your answers

Technical practice

Sensor Fusion Implementation for Inspection

What to coverDescribe your approach to fusing data from camera and LiDAR sensors for structural inspection. Discuss the trade-offs between different fusion techniques (e.g., early vs. late fusion) in the context of maritime environments.

SLAM Application in Autonomous Inspection

What to coverWhen applying SLAM for autonomous inspection, what are the primary challenges related to dynamic or feature-poor environments typical of offshore structures? How do you ensure the resulting map and localization are robust enough for production deployment?

Anomaly Detection Model Validation

What to coverHow do you validate the performance of a defect or anomaly detection model? Detail the metrics you would use beyond standard accuracy, especially when dealing with rare defects in structural inspection data.

MLOps Pipeline for Production Readiness

What to coverWalk through the key stages of an MLOps pipeline you've implemented or designed for a computer vision model. Specifically, how do you ensure the model remains production-ready on Microsoft Azure while adhering to CI/CD standards?

Image Recognition for Structural Integrity

What to coverDiscuss the architectural choices you make when designing an image recognition system for structural inspection. How do you balance model complexity against the need for real-time performance and accuracy?

Cloud AI Deployment Considerations

What to coverWhat are the critical security and data governance considerations when deploying AI solutions on Microsoft Azure for sensitive maritime inspection data? How do these requirements influence your architectural design?

Architecture Review and Code Quality

What to coverDescribe your process for reviewing the architecture and code of a robotics perception system. What specific patterns or anti-patterns do you look for to ensure the solution is scalable and maintainable?

Translating Business Problems to Technical Workstreams

What to coverGive an example of a vague operational problem from a client that you successfully translated into a structured technical workstream. What were the clear success metrics you established for that workstream?

Behavioural practice

Technical Advisory and Feasibility Assessment

What to coverDescribe a situation where a client proposed a solution that you assessed as technically infeasible. How did you conduct the feasibility assessment, and what alternative solution options did you present to them?

Managing Scope vs. Quality Trade-offs

What to coverProvide an example of when project scope, quality, and timeline were in conflict. How did you participate in managing the backlog and what trade-offs did you advocate for, and what was the outcome?

Cross-Functional Alignment

What to coverTell me about a time you had to align disparate functions—such as engineering, operations, and class/technical teams—around a single delivery plan. What challenges arose, and how did you drive consensus?

Handling Vendor/Partner Deliverable Acceptance

What to coverDescribe a collaboration with a vendor or partner where the delivered artifacts did not meet the agreed-upon acceptance criteria. What steps did you take to manage the relationship and ensure the final deliverable met the project's quality standards?

Sharpen your knowledge

Topics to prepare

End-to-End AI/Robotics Project Delivery
Understanding the full lifecycle from client problem definition through to production deployment and adoption, including managing scope, milestones, risk, and acceptance criteria. Relevance: This is the core function of the role, requiring accountability for project outcomes.
Technical Advisory in Autonomous Inspection
Grasping the technical scope of AI/Computer Vision applications in maritime/offshore inspection, including feasibility assessment and solution options for clients. Relevance: The role requires acting as a trusted technical advisor and running discovery sessions.
Robotics Perception & AI Workstreams
Deep knowledge of specific technical components like sensor fusion (camera + LiDAR), SLAM, defect/anomaly detection, and image recognition for structural inspection. Relevance: This defines the required hands-on depth in the technical direction and governance aspects of the role.
MLOps and Production Readiness
Familiarity with ensuring solutions are production-ready on Microsoft Azure or equivalent, adhering to MLOps, CI/CD, security, and data governance standards. Relevance: This covers the governance and deployment requirements for the AI solutions.

Skills to revise

Sensor Fusion Implementation
Practice integrating data streams from different sensors (e.g., camera and LiDAR) to improve perception accuracy. Relevance: Explicitly listed as a technical input area for perception workstreams.
Model Performance Validation
Conducting rigorous validation of AI model performance and evaluation methodology. Relevance: Required when reviewing architecture and code to ensure solutions are production-ready.
Technical Scoping and PoC Demonstration
Developing technical scopes and executing proof-of-concept demonstrations to support business development efforts. Relevance: A key responsibility in client engagement, translating problems into technical viability.
Backlog Prioritization and Trade-off Analysis
Simulating backlog prioritization exercises, balancing scope, quality, and timeline constraints. Relevance: Required for participating in project management activities.

Your preparation checklist

Prepare End-to-End Project Delivery Walkthrough
Develop a structured narrative detailing a past robotics/AI project, covering the entire lifecycle from initial client problem definition through to production deployment and adoption, explicitly addressing scope, milestones, risk management, and acceptance criteria.
Detail Technical Depth in Perception Systems
Be ready to discuss specific technical challenges and solutions related to sensor fusion (camera + LiDAR), SLAM, and defect/anomaly detection in the context of structural inspection, providing examples of how you directed technical decisions.
Outline MLOps and Cloud Deployment Experience
Prepare to describe a project where you ensured a solution was production-ready on Microsoft Azure or equivalent, focusing specifically on adherence to MLOps, CI/CD, security, and data governance standards.
Develop Client Advisory Scenarios
Prepare examples of running discovery sessions or requirements workshops, demonstrating how you translated ambiguous business/operational problems into structured technical workstreams with clear success metrics.
Anticipate Stakeholder Alignment Questions
Formulate responses demonstrating how you have aligned diverse groups (e.g., engineering, data, operations, class/technical functions) around a common delivery plan, including managing vendor/partner collaborations.
Prepare Thoughtful Questions for the Employer
Draft 3-5 questions focusing on the current maturity of the AI/Robotics pipeline at ABS, the typical balance between advisory vs. hands-on development, and the roadmap for adopting new autonomous inspection technologies.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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