Key AI roles:

Last Updated: [November, 2025]

AI ecosystem across IT and business organizations.

Core Engineering & Model Roles:

1. Machine Learning Engineers | Build, optimize, and deploy machine learning models into real-world production environments.

2. LLM / Generative AI Engineers | Design, build, fine-tune, and deploy Large Language Model (LLM) systems and generative AI applications.

3. NLP Engineers | Specialists who turn language into structured insight that can be fed into risk models, compliance systems, or decision-making tools.

4. Deep Learning Researchers | Push the boundaries of neural network models — design new architectures, invent new algorithms, and solve complex problems that standard machine learning can’t handle.

5. Financial Data Scientists | An analytics and modeling specialist who applies statistical, machine learning, and quantitative techniques to extract insights from financial data. Unlike generic data scientists, they understand risk, and operations, allowing them to build models that directly support decisions, risk management, and strategic planning.

Platform, Infrastructure & Architecture:

1. AI / Data Platform Architect | Designs and governs the end-to-end data and AI ecosystem that enables analytics, machine learning, and AI at scale. A liaison who connects engineering, cloud infrastructure, security, and business strategy.

2. AI / ML Platform Engineers | A specialized infrastructure engineer who builds and maintains the systems that allow data scientists, quants, and ML engineers to develop, train, deploy, and scale machine-learning models reliably and securely.

3. MLOps Engineers | To ensure AI models run reliably in production, they automate pipelines, manage deployments, monitor for drift, enforce governance, and keep models compliant with Wall Street’s strict regulatory standards. They make enterprise AI scalable, secure, and operational — turning prototypes into real business impact.

4. Vector Database Engineers | To design and operate the AI memory systems that make LLMs and RAG possible, they manage embeddings, optimize vector search, ensure top-tier security, and keep AI applications fast and reliable. They are essential for building compliant, high-performance GenAI systems used in trading, research, risk, and operations.

5. GPU / Systems Engineers | Specialize in designing, managing, and optimizing computing systems built around Graphics Processing Units (GPUs)—the hardware that accelerates AI and deep learning.

6. API & Model Serving Engineers | In modern AI infrastructure, especially in quant research and banking, training a model is only half the job. API & Model engineers can deploy, scale, and serve those models in production—reliably, securely, and with ultra-low latency.

Strategy, Risk & Governance:

1. AI Product Managers | Connect business, engineering, data science, and compliance to build AI-powered products that solve real problems; define what AI should be built, why it should be built, and how it will deliver value—especially in complex, regulated environments like large global institutions.

2. AI Strategy Leads | Responsible for defining how a bank should use AI to drive business value, competitive advantage, and organizational transformation.

3. Model Risk Officers (AI/ML) | Responsible for making sure that every AI/ML model used by the company is safe, compliant, explainable, accurate, and regulator-approved.

4. Responsible AI / Explainability Specialists | Ensure banks use AI safely, fairly, and explainably.

5. Governance & Control Framework Leads | Responsible for designing, enforcing, and monitoring the policies, controls, and structures that ensure a corporation’s technology, data, and AI systems are compliant, well-controlled, secure, audit-ready, and aligned with regulatory expectations.

Emerging Enterprise AI Roles:

1. Prompt Engineers | Ensure that LLM-powered systems work safely at enterprise scale.

2. RAG Pipeline Engineers | (Retrieval-Augmented Generation Engineer) builds and maintains systems that combine retrieval (searching internal documents/data) with generation (LLMs).

3. Agentic Workflow Designers | An emerging AI role responsible for architecting LLM-powered “agents” - systems that can plan, reason, take actions, call tools, and collaborate to complete complex tasks.

4. Automation Engineers | They bridge software engineers, RAG engineers, agentic workflow designers, ML engineers, and operations teams.

5. AI Transformation Managers | A strategic leader responsible for driving enterprise-wide adoption of AI, ensuring that AI initiatives actually deliver business impact, and coordinating the people, processes, and technology needed for large-scale AI change.