Capability Exploration: Investigate and prototype emerging capabilities such as multi-modal understanding and visual content generation.
Frontier Research: Track cutting-edge AIGC research, drive project planning, and deliver production-grade implementations. Contribute to the academic community through publications at top-tier venues.
Masters or above in Computer Science, AI, Computer Vision, Applied Mathematics, or a related field from a reputable university with strong academic credentials.
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Lead and formulate the solution and platform stack through understanding of the business needs of various Programmes and Maintenance Systems managed under Intelligence Analytics System & Anti Scam, and other Intel units in the Home Team.
Work closely with Head Intelligence Analytics Systems and Head Anti-Scam to develop solutions to meet requirements. Provide support at the various stages in the ICT Development lifecycle (AOR, AOB, Procurement and Delivery).
Define and maintain technical standards and best practices for Intelligence Analytics projects across HTDs, taking into consideration the operational concerns and constraints faced by Intel units and the data and analytics needs.
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Apply AI for document processing, data extraction, classification, decision support, and workflow optimization; ensure solutions are measurable (cycle time, accuracy, cost savings)
Build and maintain system integrations between AI solutions and enterprise systems (e.g. finance/accounting platforms, and—where applicable—TMS/WMS etc.) via APIs, files, or EDI
Work with internal IT/security teams to ensure appropriate access control, data privacy, and compliance requirements are met for each solution
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Write and maintain automated tests and CI/CD-related configurations to improve development efficiency and code quality.
Support the engineering deployment of AI/LLM capabilities based on business needs, such as integrating LLM APIs, building basic RAG pipelines, and embedding tool/agent capabilities into existing systems.
Help build and maintain datasets and benchmarks for evaluation/regression, track online performance, and assist with debugging and fixing issues.
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Design and implement the cloud-side data ingestion pipeline from edge controllers, working closely with the Embedded Software Engineer on the controller-to-cloud interface
Establish data provenance records per ingestion event — critical for IP documentation and legal defensibility
Build the cloud platform infrastructure — databases, schemas, data storage, and processing pipelines — that the broader AI system depends on
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Lead and formulate the solution and platform stack through understanding of the business needs of various Programmes and Maintenance Systems managed under Intelligence Analytics System & Anti Scam, and other Intel units in the Home Team.
Work closely with Head Intelligence Analytics Systems and Head Anti-Scam to develop solutions to meet requirements. Provide support at the various stages in the ICT Development lifecycle (AOR, AOB, Procurement and Delivery).
Define and maintain technical standards and best practices for Intelligence Analytics projects across HTDs, taking into consideration the operational concerns and constraints faced by Intel units and the data and analytics needs.
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Instrument the agent fleet with data pipelines and dashboards; apply data science techniques to understand token efficiency, failure modes, latency distribution, and business outcome correlation
Identify bottlenecks across the platform and drive measurable improvements in agent throughput, response quality, and cost efficiency — directly supporting user growth and retention
Track the research frontier — papers, open-source releases, community developments — and rapidly prototype integrations (new model capabilities, reasoning techniques, agentic frameworks)
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Collaborate with cross-functional stakeholders including engineering, manufacturing, automation, IT, and operations teams to deliver end-to-end digital solutions.
Develop solutions that support real-time data processing, event-driven architectures, and streaming analytics environments.
Participate in the development of AI-enabled applications such as conversational assistants, recommendation systems, anomaly detection, computer vision, or predictive analytics solutions.
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Develop Intelligent Scheduling Systems: Optimize workload performance and resource utilization across heterogeneous resources—CPU, GPU, memory, network, and power—spanning global data centers.
Deliver Excellence and Innovation: Produce high-quality, maintainable code while staying ahead of advancements in open-source technologies, AI/ML research, distributed systems, and serverless computing.
S./M.S, degree in Computer Science, Computer Engineering or a related area with 2+ years of relevant industry experience;
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Apply AI for document processing, data extraction, classification, decision support, and workflow optimization; ensure solutions are measurable (cycle time, accuracy, cost savings)
Build and maintain system integrations between AI solutions and enterprise systems (e.g. finance/accounting platforms, and—where applicable—TMS/WMS etc.) via APIs, files, or EDI
Work with internal IT/security teams to ensure appropriate access control, data privacy, and compliance requirements are met for each solution
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Develop and implement machine learning models (regression, classification, clustering) to predict maintenance needs and optimize operations.
Leverage Large Language Models (LLMs) to enhance data products through automated feature extraction, data enrichment, and intelligent information retrieval and decision making.
Create scalable data solutions that can handle real-time aircraft sensor data and maintenance logs.
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