Senior AI Engineer

abglobal; Nashville (TN); On-site; USD 130,000 - 190,000; Full time

Job summary

abglobal is building a next-generation enterprise AI and data platform designed to enable teams to design, deploy, integrate, and scale intelligent applications and agents across investment research, portfolio management, risk, client, and operational workflows.

The platform emphasizes AI engineering, data integration, security, CI/CD, observability, model evaluation, and production-grade validation of AI systems.

Qualifications

• 5+ years of 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.

Responsibilities

• 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.

Skills

Python

SQL

AI applications

Data pipelines

API integration

CI/CD

Security

Observability

Distributed systems

LangGraph / Agent frameworks

Tools

Azure OpenAI

OpenAI APIs

Hugging Face

Docker

Kubernetes

Job description

Who You\'ll Work With:

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.

What You\'ll Do:

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.

Responsibilities include but are not limited to:

• 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.

What We\'re Looking For:

Core AI and Data Engineering

• 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 LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, 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.

Model Hosting and Inference

• 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.


• Benchmarking and monitoring model quality, latency, availability, resource utilization, and cost.

Platform Engineering, Security, and DevOps

• Strong experience with: CI/CD, infrastructure as code, automated provisioning, and governed deployment.


• API-first and microservices architectures.


• Docker, Kubernetes, AKS, GKE, or equivalent container platforms.


• Authentication, authorization, secrets management, OAuth, managed identities, RBAC, and Microsoft Entra ID or equivalent technologies.


• Repository governance, branch protection, pull-request validation, dependency controls, and software supply-chain security.


• Metrics, distributed tracing, structured logging, monitoring, and alerting.


• Designing observable, resilient, secure, and production-ready AI systems.


• Experience using AI-assisted development tools such as Claude Code, GitHub Copilot, Cursor, Codex, or comparable tools for: Code generation, refactoring, debugging, testing, and documentation. Data-pipeline, infrastructure, and CI/CD development. Codebase understanding, modernization, architecture extraction, and pull-request review. Governed development supported by automated testing, peer review, security scanning, and controlled deployment.

Candidates must be