Top AI & Machine Learning Jobs

Architect and implement enterprise GenAI platforms (RAG and agentic systems), design knowledge retrieval, orchestration, tool-using agents and human-in-the-loop workflows, ensure production readiness and governance with product/data/security teams, define platform engineering best practices, and solve complex platform challenges with broad autonomy.

Design, build, and deploy production-grade LLM systems including fine-tuning (PEFT/LoRA/QLoRA), RAG pipelines, API orchestration, agents, inference optimization (vLLM/TGI/Triton, quantization), end-to-end ML lifecycle management, monitoring, and integration with enterprise data in a regulated financial environment.

Lead modernization of the data platform and drive Data & AI strategy. Oversee Spark-based processing, migrate to cloud-native and Apache Iceberg architectures, enable AI/ML and Agentic AI solutions, establish governance and operational KPIs, partner with stakeholders to deliver trusted data products, and build and manage a high-performing data and AI team.

Lead design and delivery of enterprise-scale GenAI and Intelligent Automation solutions (LLMs, RAG, agentic systems). Architect, implement, and support document processing, virtual assistants, and automation workflows. Ensure scalability, security, observability, CI/CD, and Responsible AI compliance while mentoring engineers and aligning solutions with enterprise architecture and stakeholders.

Design and deliver enterprise-scale Generative AI and Document AI solutions for banking and lending. Own end-to-end GenAI architecture (LLMs, RAG, orchestration), ensure security/compliance and governance, enable cloud-native microservices on Kubernetes/OpenShift, lead delivery from ideation to production, and mentor engineering teams while promoting DevSecOps and operational readiness.

The AI Data Scientist Sr designs and implements AI/ML solutions, collaborates with stakeholders, processes complex datasets, and optimizes business outcomes through data-driven analytics.

Design and implement MLOps-focused quality and automation strategies across model lifecycle, build test automation in CI/CD, validate controls, support test environments, analyze defects, and provide quality sign-off and audit readiness.

This role involves developing AI/ML architectures, resolving complex issues, and partnering with stakeholders to enhance business outcomes through technology and data.

Develop and apply valuation analytics and statistical models in marketing, supporting decision-making and budget optimization through advanced statistical techniques and data analysis.

Build and validate AML detection models using statistical and ML techniques, process and analyze large account and transaction datasets, collaborate with stakeholders and model risk teams, and communicate results to business and senior leadership.