AI Engineer

• Design, develop, and deploy AI-powered applications and intelligent services
• Build and optimize solutions using Large Language Models (LLMs)
• Develop Retrieval-Augmented Generation (RAG) pipelines and AI workflows
• Design and implement AI agents and orchestration frameworks
• Develop backend services and REST APIs supporting AI applications
• Integrate AI capabilities into enterprise software solutions
• Collaborate with cross-functional teams to define and deliver AI use cases
• Optimize the performance, scalability, and reliability of AI solutions
• Contribute to technical architecture, documentation, and engineering best practices
• Stay up to date with emerging AI technologies and industry trends

Senior AI Engineer

• Collaborate with product owners to produce clear and refined requirement definitions;
• Support architectural decisions by designing scalable architectures and exploring trade-offs;
• Guide engineers on how to effectively use AI for code generation, refactoring, debugging, optimization, reviewing, and documentation;
• Help QA engineers generate test scenarios and data, improve test coverage, and maintain test suites;
• Help DevOps engineers build and optimize pipelines, analyze logs and detect anomalies, and generate deployment documentation;
• Build reusable AI workflows, prompts, templates, and best practices;
• Lead training sessions on AI best practices and SDLC enhancements;
• Continuously refine AI workflows to support evolving engineering needs;
• Ensuring that all activities and duties are carried out in full compliance with regulatory requirements and supporting the continued implementation of the Group Anti-Bribery and Corruption Policy.

AI SDLC Engineer

• AI Systems & LLM Engineering: Design and build modern AI applications using LLMs, RAG, agents, embeddings, vector databases, and advanced prompting techniques. Develop AI workflows that are reliable, measurable, and production-ready.
• Backend Development: Build scalable backend services and APIs in Python, using frameworks such as FastAPI. Integrate AI models into existing systems with clean architecture, strong typing, validation, testing, and maintainable code.
• Data & Retrieval Infrastructure: Work with structured and unstructured data, document pipelines, embeddings, semantic search, and retrieval systems using tools such as FAISS, Pinecone, Weaviate, or similar technologies.
• Production AI & MLOps: Deploy, monitor, and improve AI systems in production. Work on evaluation pipelines, observability, latency optimization, cost control, error handling, model versioning, and CI/CD workflows.
• Cross-Functional Collaboration: Collaborate with software engineers, data scientists, product managers, and maritime experts to define requirements, validate solutions, and deliver business impact quickly.
• Documentation & Knowledge Sharing: Maintain clear technical documentation for architecture, APIs, prompts, evaluation methods, deployment processes, and operational decisions.

AI Software Engineer

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• Design, develop, and deploy AI/ML solutions into production environments
• Transform research concepts into robust, maintainable software systems
• Support research and evaluation of new technologies and products to help inform the development of innovative software solutions.
• Evaluate prototype features or components to ensure they meet functional requirements and usability standards.
• Continuously learn new technologies, tools, and development practices to stay up to date with industry trends and team standards.
• Write clean, efficient, and well-documented code, and participate in testing and debugging to ensure software quality and reliability.
• Participate in product development and engineering activities to help identify issues, propose improvements, and support resolution efforts.
• Support production monitoring and continuous improvement

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