Senior AI Engineer

The Senior AI Engineer works with the Data Engineering team under the Center of Excellence (Data Science). This role operates at the intersection of AI engineering, data engineering, agentic AI, platform engineering, and investment technology. It emphasizes Python, enterprise integration, reliable data products, security, CI/CD, observability, model and agent evaluation, and rigorous validation of production AI systems.

We are building a next-generation enterprise AI and data platform that enables teams to design, deploy, integrate, and scale intelligent applications and agents across investment research, portfolio management, risk, client, and operational workflows. The platform provides standardized infrastructure, reliable data products, commercial and self-hosted model access, retrieval, agent orchestration, evaluation, observability, security, and governance. It enables teams to rapidly deliver AI-powered capabilities in a scalable, secure, and production-ready manner.

• Build reliable, reusable, and AI-ready data products for investment and enterprise use cases.
• Design and develop production-grade AI applications, agents, and intelligent workflows using Python.
• Implement agent orchestration, tool integration, retrieval, memory, and multi-step workflows.
• Integrate AI capabilities into enterprise applications, data platforms, and business systems.
• Contribute to the shared AI and data platform that powers these capabilities at scale.

• 5+ years of experience in software, data, AI, or platform engineering.
• Strong production development experience with Python and SQL.
• Proven ability to build and operate AI applications, data pipelines, APIs, data products, and distributed systems.
• Experience integrating AI capabilities into enterprise applications and business workflows.
• Strong understanding of:
  Object-oriented and asynchronous Python APIs, microservices, and application integration Data modeling, contracts, quality, reconciliation, metadata, and lineage Batch, streaming, event-driven, and API-based architectures Workflow orchestration, distributed processing, and production operations Experience with cloud data and AI platforms such as Azure, AWS, Google Cloud, Snowflake. Ability to design scalable, reusable, resilient, and operationally supportable services.
• Generative AI and Agentic Systems Hands-on experience with:
  Large language models, embeddings, prompt engineering, context engineering, and RAG architecture. Commercial and open-source model APIs and ecosystems. AI agents, agentic workflows, reusable skills, tool calling, structured outputs, and multi-step orchestration. Vector, keyword, metadata-filtered, graph-based, and hybrid retrieval. Agent frameworks such as Lang Graph, Microsoft Agent Framework, Semantic Kernel, Auto Gen, Pydantic AI, or equivalent platforms.
• Preferred experience includes: MCP servers and clients, agent-to-agent communication, and enterprise search. Semantic layers, ontologies, and knowledge graphs. Agent memory, state management, and human-in-the-loop controls. Document intelligence and multimodal processing. Reusable agent tools, skills, manifests, and registration standards.

• Experience with one or more of the following:
  Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, or Vertex AI. Hugging Face, vLLM, Ollama, Qwen, or other approved open-source model technologies.
• Containerized model deployment and GPU-based inference. Model serving, API gateways, quantization, memory optimization, and model configuration. Context-window, token, concurrency, batching, throughput, and caching optimization.
• Benchmar…