Artificial Intelligence Engineer

Job description

Responsible for defining and managing the enterprise AI architecture to enable secure, scalable, reusable and business-aligned adoption of AI, machine learning and generative AI capabilities. The role covers AI strategy alignment, AI platforms, model and data architecture, integration patterns, MLOps, GenAI and LLM solutions, responsible AI controls, security and architecture management across enterprise transformation initiatives **Requirements** Develop and maintain enterprise AI architecture aligned with business strategy, enterprise architecture, data strategy and digital transformation goals. · Assess the current AI and analytics landscape, including models, platforms, data sources, tools, use cases, integrations, and operational capabilities. · Defining the AI architecture for the target case and transitional roadmaps for AI adoption in the organization. · Develop AI architecture principles, standards, patterns, reference architectures, and governance barriers. · Design architectures for machine learning, deep learning, generative AI, large language models, intelligent automation, and AI-enabled applications. · Define the enterprise AI platform architecture covering development, training, deployment, inference, monitoring, and lifecycle management. · Design GenAI architectures including LLM integration, augmented retrieval generation and vector databases, rapid orchestration, guardrails, and knowledge integration. · Define AI data architecture requirements including data preparation and architecture For features, feature stores, model training data, metadata, and lineage. · Define MLOps and LLMOps architecture to release, deploy, monitor, retrain, evaluate and manage models. · Design secure integration patterns between AI services, enterprise applications, APIs, data platforms, and external AI services. · Embed responsible AI, privacy, security, explainability, transparency, fairness, and human oversight requirements into architectural designs. · Define architecture requirements for model monitoring, performance, drift, observability, auditability, and operational flexibility. · Evaluate AI platforms, foundation models, cloud AI services, open source technologies, and vendor solutions. · Collaborate with enterprise, data, application, integration, cloud, security, and solutions architects to ensure end-to-end cohesive architectures. · Support AI use case evaluation by assessing feasibility, architecture complexity, data readiness, risks, scalability, and integration requirements. · Participate in architecture governance, AI governance, technical design reviews and architecture review boards. · Identify AI architecture risks, dependencies, limitations, ethical considerations, and mitigation measures. Required · Minimum experience in technology, data, software engineering, analytics, machine learning, solution architecture, or related disciplines. · A minimum of hands-on experience in AI/ML engineering, AI engineering, or data science platforms, Or a similar senior role. · Proven experience designing enterprise-level AI, machine learning, or generative AI solutions. · Experience with AI platforms, cloud AI services, model deployment, data pipelines, APIs, and enterprise integrations. · Experience with generative AI and engineering large language models is highly desirable. · Experience supporting enterprise transformation, innovation, analytics or AI adoption programs. · Experience working with senior stakeholders, data teams, engineering teams, cybersecurity teams, vendors and governance functions. Core Competencies · Enterprise AI Architecture · Machine Learning Architecture · Generative AI Architecture and LLM Architecture · AI Platform Architecture · MLOps and LLMOps · Retrieval Augmented Generation (RAG) · Vector Database and Semantic Search Architecture · AI Data Pipelines and Feature Engineering Architecture · AI Integration and API Architecture · Responsible AI and AI Governance · AI Security and Privacy · Model Monitoring and Observability · Cloud AI Architecture · Architecture Governance and Assurance Preferred Frameworks, Standards and Methods · TOGAF · ArchiMate · Cloud Architecture and Well-Architected AI Principles · Responsible AI Principles and Governance Practices · MLOps and Model Lifecycle Management Practices · Data Governance and Metadata Management Practices · API-Based Integration Patterns First and event-driven Zero Trust and secure design principles for AI environments Preferred tools and technology exposure AI and machine learning services via Microsoft Azure, AWS, Google Cloud, or similar platforms. · Generative AI and core modeling platforms, including enterprise LLM services and model orchestration technologies. · Machine learning frameworks such as PyTorch, TensorFlow, scikit-learn or equivalent. · MLOps platforms and model lifecycle management tools. · Vector databases, semantic search, embedding, and boost generation retrieval techniques. · Data platforms, data lakes, lake houses, streaming, and analytics technologies. · Containerization and orchestration technologies such as Docker and Kubernetes. · Enterprise architecture tools such as Bizzdesign, Sparx Enterprise Architect, Orbus/iServer, ARIS or similar platforms. Preferred Qualifications and Certifications · Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or related field. · Professional AI, Machine Learning, Data or Cloud Engineering certification highly desirable. · TOGAF certification is desirable. · Relevant certifications in Generative AI, Cloud AI, Data Engineering, MLOps, or Responsible AI are advantageous. Personal Attributes · Strong analytical, innovative and systems thinking ability. · Ability to translate business opportunities into practical and manageable AI architecture. · Strong communication, facilitation and management skills Stakeholders. · Ability to clearly explain complex AI concepts to technical and non-technical stakeholders. · Strong focus on security, responsible AI, data quality, scalability and architecture quality. · A consultative mindset with the ability to objectively evaluate emerging technologies and support enterprise decision-making

Requirements

• Required · Minimum experience in technology, data, software engineering, analytics, machine learning, solution architecture, or related disciplines.
• A minimum of hands-on experience in AI/ML engineering, AI engineering, data science platforms, or a similar senior role.
• Proven experience designing enterprise-level AI, machine learning, or generative AI solutions.
• Experience with generative AI and engineering large language models is highly desirable.
• Experience supporting enterprise transformation, innovation, analytics or AI adoption programs.
• Experience working with senior stakeholders, data teams, engineering teams, cybersecurity teams, vendors, and governance functions.
• Preferred Qualifications and Certifications · Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or related field.
• Professional AI, Machine Learning, Data or Cloud Engineering certification highly desirable.
• TOGAF certification is desirable.
• Ability to translate business opportunities into practical and manageable AI architecture.
• Ability to clearly explain complex AI concepts to technical and non-technical stakeholders.
• A consultative mindset with the ability to objectively evaluate emerging technologies and support enterprise decision-making

Skills