Top AI & Machine Learning Jobs

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Leads the design and delivery of cloud platforms, scalable data pipelines, and AI/ML infrastructure and applications. Responsibilities include model lifecycle management, multi-agent workflow deployment, data governance, security, monitoring, disaster recovery, and operational excellence. Provides technical leadership, mentors engineering teams, conducts architecture and code reviews, establishes AI governance standards, and collaborates with technical and business stakeholders. Drives innovation in generative AI, NLP, computer vision, and emerging technologies.

Design, train, and deploy production-grade ML and deep learning models on large healthcare datasets. Apply classical and advanced techniques, AutoML, synthetic data, and healthcare standards. Collaborate with data engineering, lead model governance, monitor performance, and communicate results to technical and non-technical stakeholders.

Design, develop, and deploy production-grade ML and deep learning models across healthcare use cases. Analyze large datasets, perform feature engineering, validate models, ensure governance and explainability, collaborate with data engineering, and communicate results to technical and nontechnical stakeholders while applying healthcare data standards and synthetic data techniques.

Leads the development and production deployment of machine learning, generative AI, LLM, RAG, and agentic AI solutions for pharmacy and healthcare operations. Builds predictive models, scalable data pipelines, AI services, vector search systems, and healthcare analytics using large clinical, claims, EHR, provider, member, and operational datasets. Establishes model monitoring, evaluation, guardrails, responsible AI practices, and regulatory compliance while collaborating with clinical, product, business, data, and engineering teams.

Leads the architecture, development, deployment, and operation of enterprise AI Ops solutions, including RAG pipelines, agentic workflows, and AI-powered applications. Improves SRE practices, leads incident response, automates cloud infrastructure with Terraform and GitHub Actions, and ensures reliability, scalability, security, and responsible AI. Designs and operates software using Python and Node.js, manages Kubernetes environments, mentors engineers, and leads globally distributed technical teams.

Develop and deliver scalable AI-powered enterprise applications and intelligent workflow integrations. Translate business needs into technical requirements, assess solution architectures, and ensure security, privacy, compliance, observability, and operational readiness. Lead AI initiatives, automate engineering workflows, mentor engineers, conduct design reviews, and promote responsible AI practices across cloud-native environments.

Direct enterprise AI and GenAI strategy, architecture standards, platform and model evaluations, Responsible AI governance, and technology roadmaps. Partner with executives, legal, compliance, security, business, product, and engineering teams to identify opportunities, assess ROI and risk, drive adoption, and align cross-organizational initiatives. Establish reusable architecture patterns, advise on major AI programs, communicate business value, and guide modernization and change efforts without direct authority.

Provides technical leadership for enterprise Generative AI and Agentic AI initiatives. Designs scalable AI platforms, foundation-model solutions, RAG and knowledge systems, multi-agent workflows, and LLMOps capabilities. Establishes architecture standards, governance, evaluation, observability, security, and responsible AI practices while optimizing quality, performance, scalability, and cost. Leads complex delivery, mentors AI professionals, influences enterprise roadmaps, and drives adoption of reusable frameworks and shared services.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.