AI Engineer

Key Responsibilities • AI Opportunity Assessment & Scoping: Work with business stakeholders to understand business problems, assess where AI can create value, determine whether AI is the appropriate solution, and translate opportunities into clearly defined use cases, requirements, scope, success measures, and implementation plans. • Technical Solution Design: Define and review solution architecture, technical requirements, data requirements, integrations, infrastructure, APIs, security considerations, and other dependencies required to implement AI solutions. • AI Development & Implementation: Develop, configure, integrate, and/or support the implementation of AI and data-driven solutions, including Machine Learning, Generative AI, Large Language Models (LLMs), and AI agents. • Technical Feasibility & Data Readiness: Assess technical feasibility, data availability and quality, integration requirements, infrastructure readiness, technical dependencies, risks, and constraints before solutions progress into development or implementation. • AI Evaluation, Testing & Assurance: Define appropriate technical evaluation frameworks, test criteria, benchmarks, and acceptance thresholds to assess model and solution performance, reliability, robustness, security, safety, bias, and other relevant risks before deployment and throughout the solution lifecycle. • Proof of Concept & Pilot Management: Support the design and execution of PoCs and pilots, including technical setup, testing, performance measurement, issue resolution, documentation, and recommendations regarding whether solutions should progress, be modified, or discontinued. • Vendor & Technology Evaluation: Technically assess vendor solutions and proposals, review architecture and technical claims, challenge assumptions, support build-versus-buy decisions, identify risks and dependencies, and ensure vendor solutions meet organizational requirements and standards. • Deployment & Operationalization: Work with relevant technical teams to transition successful solutions into production, including integration, deployment, documentation, monitoring, support models, and handover requirements. • Performance Monitoring & Continuous Improvement: Establish appropriate monitoring mechanisms and performance indicators for deployed AI solutions and support ongoing optimization, retraining, evaluation, and lifecycle management. • Project Lifecycle Technical Support: Maintain visibility of technical milestones, dependencies, risks, issues, decisions, and deliverables across multiple AI initiatives and proactively support resolution of technical barriers to implementation. • Responsible AI & Governance Technical Considerations: Embed technical requirements related to AI governance, data privacy, cybersecurity, responsible AI, regulatory compliance, human oversight, traceability, and risk management throughout the AI project lifecycle. • A Master's or PhD in Computer Science, Data Science, or a related field, showcasing a strong academic foundation in AI principles. • Proven experience of 3+ years in developing and deploying machine learning models in a professional setting. • Familiarity with programming languages such as Python, R, or Java, demonstrating the ability to write efficient and maintainable code. • Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn, indicating practical skills in model building.