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About This Role
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 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!
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
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AllianceBernstein, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
AllianceBernstein AI Hiring
AllianceBernstein has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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