Senior AI Engineer
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Before you apply
LocationNashville, Tennessee
Typefull-time
SenioritySenior
Experience5+ yrs
DepartmentData Engineering
Company size10,000+ people
PostedOct 13, 2026 · 3w ago
Verified live5d ago
At a glanceSummarised by Seekless from the posting.
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
Strong understanding of APIs, microservices, and application integration
Strong understanding of data modeling, contracts, quality, reconciliation, metadata, and lineage
Strong understanding of batch, streaming, event-driven, and API-based architectures
Strong understanding of 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
Hands-on experience with large language models, embeddings, prompt engineering, context engineering, and RAG architecture
Hands-on experience with commercial and open-source model APIs and ecosystems
Hands-on experience with AI agents, agentic workflows, reusable skills, tool calling, structured outputs, and multi-step orchestration
Hands-on experience with vector, keyword, metadata-filtered, graph-based, and hybrid retrieval
Hands-on experience with agent frameworks such as LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, Pydantic AI, or equivalent platforms
Experience with Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, or Vertex AI
Experience with Hugging Face, vLLM, Ollama, Qwen, or other approved open-source model technologies
Experience with containerized model deployment and GPU-based inference
Experience with model serving, API gateways, quantization, memory optimization, and model configuration
Experience with context-window, token, concurrency, batching, throughput, and caching optimization
Experience with benchmarking and monitoring model quality, latency, availability, resource utilization, and cost
Strong experience with CI/CD, infrastructure as code, automated provisioning, and governed deployment
Strong experience with API-first and microservices architectures
Strong experience with Docker, Kubernetes, AKS, GKE, or equivalent container platforms
Strong experience with authentication, authorization, secrets management, OAuth, managed identities, RBAC, and Microsoft Entra ID or equivalent technologies
Strong experience with repository governance, branch protection, pull-request validation, dependency controls, and software supply-chain security
Strong experience with metrics, distributed tracing, structured logging, monitoring, and alerting
Experience 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
Experience using AI-assisted development tools for data-pipeline, infrastructure, and CI/CD development
Experience using AI-assisted development tools for codebase understanding, modernization, architecture extraction, and pull-request review
Experience with governed development supported by automated testing, peer review, security scanning, and controlled deployment
Ability to independently validate AI-generated code for functional correctness and test coverage
Ability to independently validate AI-generated code for security and data-access risks
Ability to independently validate AI-generated code for performance and scalability
Ability to independently validate AI-generated code for maintainability and architectural alignment
Ability to independently validate AI-generated code for dependency, licensing, and software supply-chain concerns
Ability to independently validate AI-generated code for compliance with enterprise engineering standards
Hands-on experience with data-quality, model-evaluation, LLM-evaluation, or AI-observability frameworks
Experience building model benchmarking and evaluation pipelines
Experience building data-quality and reconciliation tests
Experience building retrieval and RAG evaluations
Experience building agent test harnesses and scenario-based simulations
Experience building continuous validation and regression testing
Experience building production monitoring for data, retrieval, model, and agent quality
Ability to measure data accuracy, completeness, freshness, and consistency
Ability to measure retrieval relevance and coverage
Ability to measure groundedness, factual consistency, citations, and provenance
Ability to measure tool selection, execution, and structured-output compliance
Ability to measure agent workflow success, failure, and escalation behavior
Ability to measure latency, throughput, reliability, resource utilization, and cost
Familiarity with OpenTelemetry, OpenInference, Datadog, Arize, LangSmith, MLflow, Microsoft Foundry evaluation capabilities, or equivalent tools
Ability to use evaluation results to inform model selection, routing, release, rollback, and production-readiness decisions
Experience with MCP servers and clients, agent-to-agent communication, and enterprise search
Experience with semantic layers, ontologies, and knowledge graphs
Experience with agent memory, state management, and human-in-the-loop controls
Experience with document intelligence and multimodal processing
Experience with reusable agent tools, skills, manifests, and registration standards
Skills
Python
SQL
Azure
AWS
Google Cloud
Snowflake
LangGraph
Microsoft Agent Framework
Semantic Kernel
AutoGen
Pydantic AI
MCP
Azure OpenAI
OpenAI APIs
Anthropic Claude
Google Gemini
Vertex AI
Hugging Face
vLLM
Ollama
Qwen
Docker
Kubernetes
AKS
GKE
OAuth
RBAC
Microsoft Entra ID
OpenTelemetry
OpenInference
Datadog
Arize
LangSmith
MLflow
Microsoft Foundry
Claude Code
GitHub Copilot
Cursor
Codex
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.
Nice to have
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 able to independently validate AI-generated code for:
•
Functional correctness and test coverage
•
Security and data-access risks
•
Performance and scalability
•
Maintainability and architectural alignment
•
Dependency, licensing, and software supply-chain concerns
•
Compliance with enterprise engineering standards
Evaluation, Observability, and Testing: Required
•
Hands-on experience with data-quality, model-evaluation, LLM-evaluation, or AI-observability frameworks.
•
Experience building:
•
Model benchmarking and evaluation pipelines
•
Data-quality and reconciliation tests
•
Retrieval and RAG evaluations
•
Agent test harnesses and scenario-based simulations
•
Continuous validation and regression testing
•
Production monitoring for data, retrieval, model, and agent quality
•
Ability to measure:
•
Data accuracy, completeness, freshness, and consistency
•
Retrieval relevance and coverage
•
Groundedness, factual consistency, citations, and provenance
•
Tool selection, execution, and structured-output compliance
•
Agent workflow success, failure, and escalation behavior
•
Latency, throughput, reliability, resource utilization, and cost
•
Familiarity with OpenTelemetry, OpenInference, Datadog, Arize, LangSmith, MLflow, Microsoft Foundry evaluation capabilities, or equivalent tools.
•
Ability to use evaluation results to inform model selection, routing, release, rollback, and production-readiness decisions.
About AB
We are a leading global investment management firm offering high-quality research and diversified investment services to institutional clients, retail investors, and private-wealth clients in major markets around the globe. With over 4,000 employees across 57 locations operating in 26 countries and jurisdictions, our ambition is simple: to be the most trusted investment firm in the world. We realize that it’s our people who give us a competitive advantage and drive success in the market, and our goal is to create an inclusive culture that rewards hard work.
Our culture of intellectual curiosity and collaboration creates an environment where you can thrive and do your best work. Whether you’re producing thought-provoking research, identifying compelling investment opportunities, infusing new technologies into our business or providing thoughtful advice to our clients, we are fully invested in you. If you’re ready to challenge your limits and empower your career, join us!
Legal
All are encouraged to apply. AB does not discriminate against any employee or applicant for employment on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability, marital status, citizenship status, sexual orientation, gender identity, military or veteran status or any other basis that is prohibited by applicable law. AB’s policies and practices seek to ensure that employment opportunities are available to all employees and applicants based solely on job-related criteria.
Nashville, Tennessee