Top AI & Machine Learning Jobs

Leads complex machine learning, advanced analytics, and AI initiatives from problem definition through production implementation. Establishes technical standards, provides direction across projects, partners with Product and Engineering, communicates findings to diverse stakeholders, evaluates emerging technologies, and mentors team members. Requires advanced Python, SQL, statistical modeling, BigQuery, cloud AI platforms, and production-grade machine learning experience.

Lead credit risk analytics and modeling engagements, develop and validate predictive models and scorecards, perform portfolio and segmentation analysis, and deliver data-driven recommendations to financial-services clients. Manage workstreams, collaborate with stakeholders, support proposals, and contribute to analytics capability development and automation initiatives.

Designs and builds production-ready AI systems using classical AI, search, planning, ranking, retrieval, knowledge representation, and agentic automation. Develops scalable APIs, microservices, data pipelines, and orchestration layers; integrates AI with enterprise applications, databases, and workflow platforms. Evaluates architectures for reliability and business impact while applying software engineering, distributed systems, cloud, observability, and security practices. Collaborates with stakeholders and contributes to design and code reviews.

Leads international data science and analytics product development, managing multiple teams of analytics managers and professionals. Oversees predictive modeling, analytical product delivery, talent development, budgeting, capacity planning, resource allocation, project management, reporting, operational policies, and stakeholder alignment. Requires management of 20+ employees, advanced analytics expertise, customer problem framing, global market knowledge, and formal presentation skills. International travel up to 50% is required.

Lead a delivery pod to implement intelligent automation, AI/ML, workflow automation, and pro-code solutions. Translate designs into scalable delivery plans, ensure governance and compliance, manage stakeholder alignment, and mentor technical teams to deliver measurable operational outcomes.

Develop predictive models and advanced analytics for credit risk, fraud, insurance, and marketing use cases. Lead client analytics engagements from design through implementation and presentation, analyze large-scale datasets, build scalable analytical frameworks, and translate findings into business recommendations. Collaborate with clients, sales teams, and internal stakeholders while supporting proposals and analytics consulting.

Design and enforce LLM guardrails and secure AI agent execution; implement cloud DLP and IAM across AWS/GCP; integrate security into CI/CD using Harness; secure artifacts in JFrog Artifactory; conduct threat modeling for LLMs, RAG pipelines, and agentic workflows; develop AI-driven security scanning.

Develop and scale AI-powered applications, developer tooling, platform services, and engineering frameworks. Build secure, compliant cloud-native solutions across application and platform layers while collaborating with engineering, product, and data science teams. Contribute to AI feature development, enterprise-scale software platforms, DevOps practices, and AI-assisted development under senior technical guidance.

Develop predictive risk management and business intelligence solutions for financial services clients. Lead descriptive, predictive, and prescriptive analytics using large datasets and statistical techniques. Program in R, Python, and SQL, present insights to executives and customers, support proposals and product adoption, and mentor junior colleagues. The role is hybrid and requires approximately 10% to 20% travel.

Owns data science and analytics workstreams from problem definition through delivery. Builds, validates, documents, and maintains scalable analytical solutions using statistical analysis, machine learning, Python, SQL, and BigQuery. Partners with technical and business stakeholders, communicates insights through reports and dashboards, automates recurring analyses, manages delivery priorities, and mentors junior team members and interns.