Top AI & Machine Learning Jobs

Lead architecture and technical strategy for production AI systems: prototype solutions, build reference implementations, define observability/evaluation/governance standards, bridge Applied AI and enterprise architecture, design integrations and cloud-native patterns, mentor engineers, and influence portfolio and partner decisions in a regulated healthcare environment.

Develop and implement quantitative, econometric, and statistical models using Python and PySpark. Optimize Spark jobs, manipulate large datasets, collaborate with business, model review, and finance teams, and communicate model design, testing, and analysis. Dataiku experience and financial industry knowledge are preferred.

Design, fine-tune, and deploy LLMs and AI agents for real-time voice borrower interactions. Build evals, data pipelines, and APIs, ensure scalability and regulatory compliance, and continuously optimize agent performance for consumer lending workflows.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Design, train, and deploy AI features (speech-to-text, entity extraction, classification) for mobile and web products. Work with noisy, domain-specific data (shop-floor audio, repair notes, scanned documents), iterate models end-to-end from research to production, monitor performance (accuracy, latency, cost), and rapidly prototype new capabilities with product and engineering.

Build, optimize and support node-based generative AI workflows for production. Develop custom Python nodes and tools, integrate and fine-tune models (adapters, ControlNet, LoRA), optimize inference and memory, ensure temporal consistency for image/video generation, package reusable graph templates, and provide technical support and documentation to artists and production teams.

Build production systems that probe and modify LLM internals to improve reliability and enforce deterministic policy. Implement mechanistic-interpretability techniques (activation patching, control vectors), work with weights/activations, build evaluation and deployment loops, and design feature-level intervention systems for enterprise inference.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Design, develop, and deploy custom ML models and transformer-based NLP systems. Build and maintain ML pipelines, fine-tune LLMs, implement RAG, monitor production performance, research new techniques, and mentor junior engineers.

Build, operate, and secure the firm's AI infrastructure and developer tooling to enable Python-based AI applications. Implement Azure AI Foundry, integrate LLM APIs (Anthropic, Azure OpenAI), design data pipelines (ADF, Databricks), manage cloud infrastructure (IaC, Kubernetes), and enforce security, governance, and compliance for regulated financial environments.