Top AI & Machine Learning Jobs

Develops predictive models and transforms structured and unstructured data into actionable insights. Responsibilities include data wrangling, statistical analysis, machine learning, visualization, reporting, data pipeline management, cloud and DevSecOps integration, root-cause analysis, and technical solution development. The role supports government stakeholders, guides junior personnel, leads data projects independently, validates AI models, documents algorithms, and communicates findings through briefings and presentations.

A full-time, 12-week Data Science Internship involving analytics projects, dashboards, models, and business insights. The intern will use SQL, Python, and Power BI, collaborate with analytics and cross-functional teams, develop professional skills, and present project outcomes to senior leadership.

Research and develop omni-modal multimodal large models, including training data, foundational algorithms, pre-training, supervised fine-tuning, reinforcement learning, evaluation, and downstream applications. Analyze performance bottlenecks, develop first-principles solutions, explore next-generation architectures, and advance multimodal understanding and generation. The role also involves optimizing deep learning operators, model training and inference, CPU/GPU acceleration, and distributed systems.

Lead the architecture, build, and deployment of AI-driven automation and intelligent systems to solve security, compliance, and risk challenges. Develop real-time intelligence, workflow automation, and risk-based decision-making tools, and engage customers and partners to refine and deploy production-grade solutions.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Lead the brand’s AI strategy, roadmap, use-case prioritization, and high-priority initiative delivery. Build business cases, develop prototypes, drive AI adoption and training, evaluate vendors and platforms, manage responsible-AI governance, and report portfolio value and risks to executives. The role partners across business, technology, legal, and parent-company teams to embed AI into workflows and deliver measurable results.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

Design, build, and deploy production-grade agentic AI systems for regulated banking use cases. Responsibilities include multi-agent orchestration, language-model fine-tuning, evaluation frameworks, MCP and A2A integrations, enterprise API development, cloud deployment, observability, and rapid prototyping. The role requires independently delivering tested, production-ready systems while collaborating with researchers, data scientists, and business stakeholders.

Build production-grade AI software, microservices, libraries, and data pipelines. Partner with data and backend teams to operationalize AI models, expose capabilities through APIs, deploy workloads using cloud infrastructure and Kubernetes, and maintain testing, code quality, and CI/CD standards. The role also involves ETL/ELT, database integration, and understanding AI concepts including inference, embeddings, LLMs, and RAG architectures.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.