Top AI & Machine Learning Jobs

Lead definition, design, and delivery of AI-powered conversational virtual assistants and proactive outbound contact center solutions. Translate business capabilities into features, manage product backlog, partner with engineering and data science to define technical requirements and test strategies, evaluate emerging AI technologies, and track product performance to drive iterative improvement.

Leads and mentors software engineers building secure, cloud-native AI applications. Designs and deploys AI agents, LLM workflows, RAG systems, and shared agent frameworks using Microsoft Azure. Owns AI-centric SDLC practices, including governance, evaluation, CI/CD, observability, testing, guardrails, and incident response. Embeds security, privacy, compliance, identity, encryption, and least-privilege controls into AI systems while contributing production code and collaborating with product, platform, security, and compliance teams.

Design, develop, and deploy AI/ML models (including NLP and LLM solutions) for Sales and Digital use cases; analyze data, build model APIs, collaborate cross-functionally, and translate AI research into production-ready capabilities while ensuring ethical, transparent model development.

Senior Data Scientist applying advanced analytics and AI/ML to optimize healthcare contact center sales, Medicare conversion, DTC engagement, intelligent routing, agent performance, and conversation workflows. The role analyzes contact center and customer journey data, translates business needs into technical requirements, validates solutions through UAT and release readiness, and partners with stakeholders across sales, operations, product, engineering, QA, and architecture.

Design, develop, and operationalize AI/ML, Generative AI, LLM, and agentic AI solutions. Build scalable batch, streaming, and cloud-native data pipelines using PySpark/Scala on Azure. Implement MLOps, CI/CD, model monitoring, RAG/vector search, and production-grade LLM applications. Collaborate with data science, architecture, and business teams, evaluate emerging AI tech, and produce prototypes, POCs, and documented deployments.

Design, build, and maintain BI and analytics solutions, transform and validate large datasets, develop ML and Generative AI models, create dashboards and reports, optimize data pipelines, and present insights to stakeholders while supporting data governance and Agile practices.

Design, build, operate, and maintain enterprise AI platform services and agent runtimes. Develop APIs, SDKs, MCP servers, tool interfaces, retrieval and embedding pipelines, model serving, evaluation infrastructure, and distributed data foundations. Establish engineering, security, testing, CI/CD, and infrastructure standards while optimizing platform cost and scale. Apply AI/ML capabilities to healthcare workflows, ensure PHI protection and tenant isolation, evaluate emerging technologies, and mentor engineers while influencing technical strategy.

Lead the architecture and end-to-end development of production-grade AI/ML systems, including training, deployment, monitoring, MLOps, deep learning, NLP, ASR, and agentic AI workflows. Translate research into scalable enterprise solutions, establish engineering and governance standards, evaluate emerging tools, communicate technical tradeoffs to leadership, and mentor engineers. The role requires expertise in Python, deep learning frameworks, cloud ML platforms, ML pipelines, statistics, and production LLM or agentic systems.

Lead data science projects from design through implementation, delivering statistical and advanced analytics solutions with minimal supervision. Collaborate with data science leads, business partners, and healthcare stakeholders to understand data, design solutions, operationalize analytics, and communicate progress. Apply regression, time-series analysis, clustering, decision trees, SVM, gradient boosting, and NLP using Python, Azure, and Databricks to solve complex healthcare business problems.

Leads the architecture, development, deployment, and governance of production-grade machine learning and generative AI solutions. Responsibilities include hands-on work with traditional ML, deep learning, LLMs, NLP or computer vision, MLOps, cloud infrastructure, model security, and responsible AI. The role leads proof-of-concept initiatives, transitions models into scalable production systems, mentors technical teams, documents architecture, and advises cross-functional stakeholders and leadership.