Top AI & Machine Learning Jobs

Designs and optimizes AI-powered workflows using LLMs, agents, prompts, tools, context, and orchestration frameworks. Translates business challenges into scalable multi-step workflows, integrates solutions with enterprise applications, troubleshoots workflow and agent issues, analyzes performance data, develops automation utilities, and supports AI governance, risk management, responsible AI, monitoring, and quality assurance.

Designs and optimizes AI-powered workflows using LLMs, agents, prompts, orchestration frameworks, and enterprise integrations. Translates business challenges into scalable multi-step workflows, manages context and memory, troubleshoots agent behavior and integrations, evaluates performance, and develops automation for testing and monitoring. Partners with business, product, technology, and operations teams while supporting AI governance, risk management, quality assurance, and responsible AI practices.

Leads complex, enterprise-scale Generative AI engineering initiatives. Designs, codes, tests, debugs, documents, and reviews large-scale AI solutions while establishing engineering standards and best practices. Develops scalable platforms using foundation models, RAG, agentic AI, and multi-agent systems; provides technical leadership, mentorship, governance, security, observability, evaluation, and operational excellence across AI deployments.

Leads quantitative modeling and analytics initiatives supporting risk assessment, market monitoring, model validation, and business decisions. Develops and evaluates machine learning, deep learning, GenAI, and RAG solutions; conducts data mining, forecasting, trend analysis, and exploratory analysis. Communicates findings, model risks, and recommendations to technical and business stakeholders, collaborates with auditors and regulators, and guides junior analysts.

Leads implementation of shared AI enablement capabilities for coding agents across development, DevOps, and RunOps. Builds secure MCP integrations, AI-assisted development workflows, CI/CD automation, ephemeral environments, telemetry-driven incident response, operational runbooks, and compliance guardrails. Translates architecture into reusable engineering capabilities, mentors teams, drives adoption through workshops and pair programming, and measures reliability, code acceptance, deployment performance, and AI resource usage.

Leads hands-on development of shared AI enablement capabilities for coding agents across development, DevOps, and operations. Builds secure MCP integrations, agent-assisted workflows, CI/CD automation, ephemeral environments, observability and incident-response automation. Establishes guardrails, audit controls, reusable playbooks, and operational standards while coaching engineering teams and measuring adoption, reliability, and performance.

Leads hands-on implementation of shared AI enablement capabilities for coding agents across development, DevOps, and RunOps. Builds secure MCP integrations, agent-assisted development workflows, CI/CD automation, ephemeral environments, telemetry-driven incident remediation, and operational playbooks. Establishes engineering guardrails, monitors reliability and adoption metrics, supports compliance controls, and coaches teams through workshops and pair programming.

Leads enterprise strategy, architecture, and implementation of Generative AI, agentic engineering, and AI-augmented software development across the SDLC. Designs AI agents, multi-agent systems, RAG capabilities, developer platforms, and intelligent automation integrated with CI/CD, IDEs, portals, and release workflows. Establishes governance, observability, standards, and responsible AI controls while influencing senior technology leadership and mentoring principal engineers and architects.

Lead end-to-end development, validation, and governance of machine learning and statistical fraud models. Analyze large-scale transaction data, engineer predictive features, evaluate model performance, and deploy solutions in distributed environments. Conduct independent validation, testing, documentation, monitoring, and effective challenge while partnering with fraud, technology, risk, audit, governance, and business teams. Present findings to technical and non-technical stakeholders, support regulatory reviews, mentor analysts, and help advance responsible AI and GenAI applications for fraud prevention.

Leads development, validation, and production implementation of machine learning and statistical models for fraud detection. Analyzes large-scale transaction data, engineers predictive features, evaluates model performance, performs independent validation, and documents governance evidence. Partners with fraud, technology, risk, audit, regulatory, and business teams while mentoring quantitative analysts and addressing emerging fraud trends.