About Invesco

As one of the world’s leading independent global investment firms, Invesco is dedicated to rethinking possibilities for our clients. By delivering the combined power of our distinctive investment management capabilities, we provide a wide range of investment strategies and vehicles to our clients around the world. If you're looking for challenging work, intelligent colleagues, and exposure across a global footprint, come explore your potential at Invesco.

About the Role:

We are seeking an experienced and innovative Senior AI Developer to help accelerate the adoption of Artificial Intelligence across Investments Technology. This role is ideal for a hands-on engineer who is passionate about emerging AI technologies and enjoys turning new ideas into practical solutions that deliver business value.

The successful candidate will play a key role in designing and building enterprise AI capabilities that support investment, research, risk, performance, and distribution functions. They will work closely with architects, engineers, product owners, and business stakeholders to develop scalable AI solutions, establish reusable patterns, and promote modern AI engineering practices across the organization.

A major focus of this role will be leading the implementation of a common semantic layer that enables consistent access to investment and distribution data, developing AI agents that support investment workflows, and helping teams adopt Spec-Driven Development and other AI-assisted engineering approaches. The individual will be expected to stay current with the rapidly evolving AI landscape, evaluate new technologies, and share their expertise to help elevate the technical capabilities of the broader development team. This role combines hands-on delivery, technical innovation, and thought leadership while remaining deeply involved in the implementation of solutions.

Responsibilities of the Role:

AI Platform Development

• Build reusable AI platform components that can be leveraged across multiple business capabilities.
• Integrate AI solutions with enterprise data platforms, model platforms, and business applications.
• Support governance, security, observability, and operational requirements for production AI solutions.
• Contribute to the development of a scalable and maintainable AI ecosystem that supports both experimentation and enterprise adoption. 

Semantic Layer Development

• Lead the design and implementation of a common semantic layer supporting Investments and Distribution use cases.
• Define semantic models, taxonomies, metadata structures, and business definitions that improve data accessibility and consistency.
• Work with data, engineering, and business teams to ensure a governed and reusable data foundation for AI solutions.
• Support natural language access to investment data through semantic and AI services.

AI Agent Development

• Design and develop AI agents that support investment research, portfolio management, risk analysis, performance analytics, product intelligence, and distribution workflows.
• Build reusable agent frameworks, orchestration patterns, and integration approaches.
• Develop multi-step AI workflows that combine structured and unstructured data sources.
• Establish approaches for agent evaluation, testing, observability, and continuous improvement. 

AI Innovation & Technical Excellence

• Stay current with emerging AI technologies, frameworks, and industry trends, identifying opportunities to apply them within Investments Technology.
• Evaluate new tools and platforms through proof-of-concepts and practical experimentation.
• Contribute to AI architecture decisions and implementation standards.
• Share knowledge with development teams through mentoring, demonstrations, technical workshops, and documentation.
• Promote engineering best practices for building scalable, secure, and maintainable AI solutions.

Spec-Driven Development & AI Engineering

• Help introduce and scale Spec-Driven Development practices within Investments Technology.
• Leverage AI-assisted development tools to improve productivity and software quality.
• Contribute to standards for specification creation, automated testing, and AI-assisted delivery workflows.
• Develop reusable templates, reference implementations, and engineering patterns that accelerate solution delivery.

Requirements of the Role:

• Strong software engineering experience with demonstrated delivery of enterprise applications.
• Hands-on experience designing and developing Generative AI, agentic AI, or machine learning solutions.
• Experience working with modern AI frameworks, orchestration platforms, and large language models.
• Strong understanding of enterprise application architecture and integration patterns.
• Experience working directly with business stakeholders to translate requirements into technical solutions.
• Excellent problem-solving, communication, and collaboration skills.
• Passion for continuous learning and applying emerging technologies.

Preferred Experience

• Experience within asset management, wealth management, investment management, research, trading, risk, or financial services.
• Experience developing semantic models, knowledge layers, metadata frameworks, or business taxonomies.
• Experience building enterprise AI agents and AI-enabled workflow solutions.
• Experience implementing AI governance, evaluation, and observability capabilities.
• Experience operating across Snowflake, AWS, and Microsoft AI ecosystems.

Key AI Technologies & Expertise:

Enterprise AI Platforms

• Snowflake Cortex, Analyst, Search
• Snowflake Cortex Code
• AWS Bedrock, AgentCore, etc.
• Azure Agent Foundry
• Microsoft Copilot Studio
• Anthropic Claude

Agentic AI

• LangGraph
• LangChain
• Strands
• AutoGen
• CrewAI
• Model Context Protocol (MCP)
• Multi-Agent Systems
• Agent Orchestration Frameworks

Retrieval & Knowledge Systems

• Retrieval-Augmented Generation (RAG)
• GraphRAG
• Enterprise Search
• Semantic Search
• Knowledge Graphs
• Vector Search and Embeddings
• Hybrid Retrieval Architectures

Semantic Layer & Data Intelligence

• Semantic Modeling
• Business Taxonomies
• Metadata Management
• Ontology Design
• Data Catalogs
• Knowledge Representation
• AI Data Governance

AI Engineering & Operations

• Spec-Driven Development (SDD)
• AI-Assisted Development
• AI Coding Agents
• LLM Evaluation Frameworks
• Prompt Engineering
• Context Engineering
• AI Observability
• AI Guardrails and Safety Controls
• Human-in-the-Loop Workflows
• LLMOps

Full Time / Part Time

Full time

Worker Type

Employee

Job Exempt (Yes / No)

Yes

Workplace Model

Pursuant to Invesco’s Workplace Policy, employees are expected to comply with the firm’s most current workplace model, which as of October 1, 2025, includes spending at least four full days each week working in an Invesco office. This reflects our belief that spending time together in the office helps us build stronger relationships, collaborate more easily, and support each other’s growth and development.

The above information on this description has been designed to indicate the general nature and level of work performed by employees within this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job. The job holder may be required to perform other duties as deemed appropriate by their manager from time to time.

Invesco's culture of inclusivity and its commitment to diversity in the workplace are demonstrated through our people practices. We are proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender, gender identity, sexual orientation, marital status, national origin, citizenship status, disability, age, or veteran status. Our equal opportunity employment efforts comply with all applicable U.S. state and federal laws governing non-discrimination in employment.