AI Engineer-MNC Financial Services

Summary: Reporting to the AI & Technology Oversight Manager the AI Engineer is responsible for embedding artificial intelligence capabilities into automation and assurance ecosystems. Acting as a bridge between cuttingedge AI technologies and existing highcode and lowcode platforms the role focuses on AI enablement rather than foundational model building ensuring intelligence is thoughtfully integrated into systems and workflows. The AI Engineer designs develops and assures AI-enabled solutions improves automation efficiency elevates engineering quality and mentors teams on responsible and effective AI adoption. The mission is to drive innovation productivity and intelligent automation across the organization while upholding compliance security and architectural integrity. The role requires strong handson engineering skills practical understanding of agentic AI patterns and the ability to guide teams on effective and responsible AI usage.

Job Responsibilities :

AI Enablement and Integration:

Hands-on contributor to the design and development of AI-enabled solutions capable of writing both production-quality code and rapid experimental prototypes.

Develop and implement AIenabled microservices APIs applications and internal tools.

Integrate AI capabilities following secure scalable engineering best practices.

Design build and validate AIdriven solutions leveraging providers such as OpenAI and Anthropic.

Enhance lowcode/nocode automation platforms (e.g. Power Automate n8n Workato) by embedding intelligent processing and applying agentic patterns where relevant.

Implement Model Context Protocol (MCP) servers for secure AItosystem connectivity.

Lead AIbased document parsing and intelligent data extraction initiatives.

Contribute to educating and enabling Enterprise Capabilities areas including Integration and Automation by providing guidance training and best practices e.g. on effective use of n8n agents.

Engage with business stakeholders to understand requirements constraints and key drivers identifying and implementing highvalue AI opportunities across Waystone.

AI Engineering:

Prototype AI features and iterate towards productionready capabilities.

Build agentic workflows using frameworks such as LangChain or Microsoft Agent Framework with a solid understanding of agent fundamentals (tools memory orchestration context control).

Implement AI agents with tool integration memory context control and guardrails.

Develop retrievalaugmented workflows to enhance context reliability and performance.

Perform quality assurance on AI outputs by implementing robust AI observability practices including monitoring model behaviour detecting anomalies and ensuring visibility into AI performance and reliability.

Contribute to ongoing research and development staying current with emerging AI tools frameworks and techniques to identify opportunities for innovation and improvement.

Apply sound judgment to determine when not to use AI ensuring traditional deterministic solutions are chosen when they are safer simpler or more cost effective.

Ensure AI-enabled solutions consider full total cost of ownership including token consumption performance observability and ongoing maintenance with awareness of costefficiency and modelselection tradeoffs.

Knowledge Sharing Mentoring and Governance:

Mentor and support both technical and nontechnical staff (e.g. citizen developers) fostering knowledge sharing and strengthening AI fluency across Waystone.

Act as AI subject matter expert for engineering testing architecture teams as well as business functions across the wider organisation. Deliver demos internal evangelism and produce reference documentation.

Nurture the wider internal community helping uplift AI adoption and responsible highvalue usage across the business.

Lead the design and documentation of AI-enabled solutions contributing to Solution In Principle (SIP) or Solution Architecture Design (SAD) documents as needed.

Collaborate with delivery teams throughout the project lifecycles offering guidance on AI-enabled solutions and addressing technical challenges as they arise.

Develop internal best practices for prompt engineering AI-processed data handling AI-assisted coding creation and sharing of custom agents and responsible AI usage.

Contribute to AI governance ethics compliance and risk control activities.

Ensure AI solutions comply with enterprise architecture principles security policies data governance standards human-in-the-loop controls and regulatory requirements.

Monitor and mitigate AI risks raising concerns early and recommending remedial actions.

Candidate Profile :

To perform this job successfully an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Knowledge Skills and Abilities:

Deep understanding of the distinction between Generative AI and Agentic AI including their foundations capabilities and appropriate use cases.

Strong understanding of AI ML and LLM concepts including prompt engineering prompt grounding iterative loop techniques context windows embeddings RAG agentic workflows.

Proven ability to integrate AI capabilities both into low-code automation flows and high-code stacks including applications APIs microservices distributed systems and development or testing tools.

Solid software development background with hands-on coding experience in one or more engineering ecosystem such (C#) Python or TypeScript.

Excellent communication skills with the ability to translate complex AI concepts for nonexperts and to effectively influence and collaborate with stakeholders at all levels both technical and nontechnical.

Strong writing skills with the ability to contribute to AI literacy and AI fluency documentation.

Strong understanding of responsible AI principles including governance bias mitigation compliance and risk-based decision-making.

Analytical thinking with excellent problemsolving ability and keen attention to details.

Ability to mentor developers and testers and to drive innovation across engineering QA and architecture.

Ability to assess AIenabled capabilities in thirdparty SaaS platforms (e.g. Appian Salesforceetc) and provide guidance on responsible effective adoption.

Experience:

5 years development experience across APIs integrations microservices or fullstack development.

Demonstrated realworld experience supported by a portfolio of work that highlights applied skills solution delivery and measurable impact including personal or opensource AI projects where applicable.

Solid experience integrating AI into workflows and systems across both lowcode and highcode platforms.

Handson use of AI coding assistants (e.g. GitHub Copilot Claude Code) and autonomous software engineering agents.

Exposure to RAG vector databases embeddings and AI retrieval systems.

Experience working with cloud AI services and orchestrating AI agents.

Exposure to DevOps practices CI/CD pipelines infrastructure-as-code and cloud platforms such as Azure or AWS in highly regulated enterprise environments.

Extensive experience with source control and version management systems.

Education:

Degree in Computer Science IT Engineering or a related discipline (or equivalent practical experience).

Professional certifications in AI Fluency or specialised AI / ML technologies are advantageous although strong selfdirected learning and practical AI experience are equally valued.

Required Skills: