Overview
The Associate Senior AI Solutions Officer will be responsible for designing, developing, implementing, and operationalizing AI-powered business solutions to address business needs and improve operational outcomes.
Key Responsibilities
- Design, build, and deploy AI-powered solutions.
- Translate business requirements into scalable, secure, and production-ready AI applications.
- Develop prototypes, pilots, and enterprise-grade solutions using Generative AI, Agentic AI, machine learning, and automation technologies.
- Deliver AI solutions supporting due diligence, risk management, knowledge management, and operational workflows.
- Design and implement AI agents, multi-agent workflows, and intelligent automation solutions.
- Build conversational assistants, decision-support tools, document processing solutions, and workflow automation capabilities.
- Integrate AI agents with enterprise systems, tools, and business processes.
- Ensure Responsible AI principles, governance controls, and human oversight are incorporated into all solutions.
- Build Retrieval-Augmented Generation (RAG) and knowledge discovery solutions leveraging enterprise data and content.
- Implement semantic search, vector search, knowledge graphs, and enterprise knowledge repositories.
- Enable trusted, secure, and efficient access to organizational knowledge.
- Partner with data engineering teams to ensure data platforms are AI-ready.
- Integrate AI solutions with enterprise data platforms, lakes, warehouses, and semantic models.
- Promote data quality, governance, lineage, security, and compliance practices that support trusted AI.
- Integrate AI capabilities into enterprise applications, workflows, and digital platforms.
- Develop APIs, event-driven integrations, and reusable AI services.
- Collaborate with software, platform, and solution engineering teams to deliver scalable enterprise solutions.
- Support the deployment, monitoring, optimization, and lifecycle management of AI solutions.
- Implement security, privacy, compliance, and AI governance requirements.
- Establish observability, performance monitoring, and operational support processes for AI services.
- Drive adoption of AI capabilities across business and operational teams.
- Provide training, guidance, and best practices for responsible AI use.
- Partner with stakeholders to identify opportunities and maximize business value from AI investments.
- Evaluate emerging AI technologies and identify opportunities to improve business outcomes.
- Lead experimentation, proof-of-concepts, and innovation initiatives.
- Serve as a trusted technical advisor on AI strategy, architecture, and solution development.
- Contribute to AI standards, best practices, and enterprise AI capabilities.
Required Experience
- At least 8 years of relevant professional experience with a Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related field; or at least 10 years of relevant professional experience with a Bachelor’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related field; or an equivalent combination of education and relevant professional experience.
- 7+ years of experience in software engineering, data engineering, AI solution development, or solution architecture.
- Proven experience delivering AI, automation, or data-driven solutions in enterprise environments.
- Experience working directly with business stakeholders to translate requirements into technology solutions.
- Experience designing and implementing enterprise solutions on AWS, Azure, or Google Cloud.
- Strong understanding of APIs, microservices, event-driven architectures, and system integration patterns.
- Experience building scalable, secure, and production-ready applications and platforms.
- Hands-on experience developing and deploying Generative AI and Agentic AI solutions.
- Practical knowledge of LLMs, prompt engineering, AI agents, orchestration frameworks, and Retrieval-Augmented Generation (RAG).
- Experience delivering AI assistants, knowledge search solutions, document intelligence, workflow automation, or decision-support applications.
- Understanding of Responsible AI principles and AI governance practices.
- Experience building knowledge retrieval and enterprise search solutions using vector search, semantic search, or RAG architectures.
- Familiarity with enterprise content repositories, knowledge graphs, or document management platforms.
- Ability to design solutions that enable trusted and secure access to organizational knowledge.
- Experience working with enterprise data platforms, data lakes, warehouses, and analytics environments.
- Familiarity with Databricks, Microsoft Fabric, Azure Data Lake, AWS data services, or similar platforms.
- Understanding of data governance, metadata management, data quality, and AI-ready data architectures.
- Strong programming skills in Python and modern software engineering practices.
- Experience integrating AI services with enterprise applications, APIs, and digital platforms.
- Familiarity with containerization, serverless technologies, CI/CD pipelines, and cloud-native development approaches.
- Experience with low-code platforms such as OutSystems is an advantage.
- Understanding of MLOps, LLMOps, monitoring, observability, and model lifecycle management.
- Experience implementing security, privacy, and compliance controls within enterprise technology environments.
- Knowledge of AI governance, explainability, auditability, and responsible AI practices.
- Experience delivering AI solutions in financial services, development organizations, risk management, compliance, or regulated environments is preferred.
- Experience with AWS Bedrock, SageMaker, Azure OpenAI, Azure AI Foundry, Microsoft Copilot Studio, or similar enterprise AI platforms is preferred.
- Knowledge of Knowledge Graphs, Semantic Layers, Digital Twins, or Intelligent Automation platforms is preferred.
- Familiarity with Agile, SAFe, Product Operating Models, or enterprise delivery frameworks is preferred.
- Relevant cloud, AI, or architecture certifications are desirable.
Qualifications
• Master’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related field, plus at least 8 years of relevant professional experience; or Bachelor’s degree in Computer Science, Software Engineering, Data Science, AI/ML, or a related field, plus at least 10 years of relevant professional experience; or an equivalent combination of education and relevant professional experience.
