AI Solution Engineer

The AI Solutions Engineer is a hands-on technical engineer responsible for accelerating software engineering delivery through the effective adoption of Artificial Intelligence and modern engineering practices.

Working closely with Engineering Leads, Solution Architects, Product Owners and software engineering teams, you will identify opportunities to improve engineering productivity through AI-assisted development while maintaining Fidelity's high standards for architecture, engineering governance, software quality and security.

You will combine strong software engineering expertise with practical knowledge of Large Language Models (LLMs) and AI-powered development tooling to establish best practices for AI-assisted software engineering. This includes to make effective use of technologies such as Claude Code, GitHub Copilot and other AI coding assistants to improve software design, implementation, testing, documentation and operational support.

The role is equally focused on engineering excellence and innovation. You will apply modern software and AI engineering practices through hands-on design, development, testing, deployment, and production support, contribute to architectural decisions within established standards, support the adoption and continuous improvement of CI/CD, DevSecOps practices and implement approved security, governance, and Responsible AI controls throughout the software development lifecycle.

This is an exciting opportunity to shape how AI is adopted across Asset Management Platforms, helping engineering teams deliver high-quality software faster while ensuring solutions remain secure, maintainable and aligned with Fidelity's engineering standards.

Key Responsibilities

AI Engineering Enablement

• Design, build, deploy, and support enterprise AI applications using LLMs, RAG, embeddings, vector search, tool calling, and agentic workflows.

• Build grounded RAG pipelines covering document ingestion, parsing, chunking, metadata enrichment, hybrid retrieval, reranking, citation, and response validation.

• Develop AI agents capable of planning, reasoning, maintaining context, invoking approved tools, and executing governed business workflows.

• Integrate commercial and open-source models through platforms such as AWS Bedrock, Azure OpenAI, OpenAI, Anthropic, or equivalent services.

• Implement prompt and model configuration management, evaluation datasets, automated quality testing, guardrails, and human-in-the-loop controls.

• Evaluate AI technologies progmatically and select solutions based on business value, quality, security, latency, scalability, and cost.

• Coach and mentor engineering teams in the effective use of Claude Code, GitHub Copilot and other AI-assisted development tools.

Software Engineering

• Build complete AI-assisted apps using modern frontend technologies such as Angular or React, TypeScript, JavaScript, HTML, and CSS.

• Develop secure and scalable APIs, microservices, and orchestration services using Python, FastAPI, Node.js, or NestJS.

• Integrate applications with enterprise APIs, market and research data, document repositories, collaboration platforms, and structured and unstructured data sources.

• Work with Relational, NoSQL, Cloud and vector technologies such as PostgreSQL, OpenSearch, Snowflake and pgvector.

• Participate in engineering practices, including modular design, API contracts, code reviews, performance optimization and secure coding.

Engineering Excellence

• Champion modern engineering practices including Continuous Integration, Continuous Delivery and DevSecOps.

• Work with engineering teams to optimise software delivery pipelines.

• Promote unit test automation and engineering best practises throughout the SDLC.

• Create reusable AI services, reference implementations, engineering standards, and ready-to-use development capabilities that improve developer productivity.

Architecture & Governance

• Collaborate with Solution Architects and Engineering Leads to ensure solutions align with Fidelity's target architecture.

• Ensure AI-assisted software development complies with engineering standards, security policies and regulatory requirements.

• Promote sound software engineering principles including maintainability, scalability, resilience and observability.

• Implement controls for prompt injection, sensitive-data exposure, unsafe responses, hallucination, unauthorized tool use, and third-party model risk.

• Balance innovation with appropriate technology risk management and governance.

• Communicate technical decisions, risks, trade-offs, and delivery progress clearly to engineering, architecture, product, and business stakeholders.

Stakeholder Engagement

• Build strong working relationships with Engineering Leads, Product Owners, Solution Architects and software engineering teams.

• Partner with investment technology stakeholders to understand engineering challenges and identify opportunities for AI-enabled improvement.

• Establish and support communities of practice around AI-assisted software engineering.

• Deliver demonstrations, technical workshops and knowledge-sharing sessions with senior investment professionals.

• Stay informed on emerging AI technologies and assess their applicability within Fidelity's engineering environment.