Top AI & Machine Learning Jobs

Leads the design, development, deployment, and monitoring of scalable production AI/ML solutions. Responsibilities include data preparation, feature engineering, model development and optimization, ML pipeline implementation, drift analysis, observability, responsible AI, and multi-cloud infrastructure. The role collaborates with engineering, product, data science, and UX teams to deliver technical solutions and integrate batch and real-time data pipelines.

Leads end-to-end integration, architecture, development, validation, and deployment of AI-powered enterprise applications in the Clinical AI domain. Translates business needs into scalable solutions, evaluates technology options, manages technical workstreams, and ensures security, privacy, compliance, observability, resilience, and responsible AI standards. Coordinates proofs of concept and production readiness while mentoring engineers and advancing automation, AI-assisted workflows, and engineering best practices.

Design, develop, and maintain scalable software and AI-powered solutions. Collaborate with cross-functional teams, implement cloud-native architectures, CI/CD pipelines, containerization, and streaming data pipelines. Mentor junior engineers and participate in AI/ML POCs and roadmap initiatives while ensuring test coverage via ATDD and following Agile practices.

Design and build scalable full-stack applications using Java (Spring Boot), React.js/React Native, and AWS. Integrate LLMs and GenAI for smart search, recommendations, conversational interfaces; apply AI-assisted development (GitHub Copilot), TDD, CI/CD, and mentor teams while participating in architecture, code reviews, and Agile delivery.

Designs and builds scalable, secure AI/ML solutions for healthcare clients, including LLM workflows, AI agents, copilots, and intelligent automation. Leads solution architecture, prototyping, cloud deployment, evaluation testing, troubleshooting, and production delivery. Translates ambiguous business and clinical needs into actionable technical solutions while advising stakeholders and collaborating across product and engineering teams.

Leads and mentors an AI/ML engineering team developing, deploying, and maintaining enterprise solutions. Sets technical strategy, architecture, engineering standards, MLOps practices, and software delivery processes. Partners with business, product, and architecture leaders to deliver AI-driven operational and customer experience solutions. Oversees resources, schedules, risks, governance, security, compliance, model performance, system availability, and team productivity.

Leads the architecture, development, deployment, and governance of production machine learning and generative AI solutions. The role drives GenAI proof-of-concepts, establishes MLOps and responsible AI practices, mentors engineers, conducts technical reviews, and collaborates with cross-functional teams to deliver scalable AI capabilities. Expertise is required in statistical methods, deep learning, NLP or computer vision, cloud-native infrastructure, and production ML systems.

Designs and deploys enterprise AI/ML solutions, scalable models, data pipelines, and MLOps architectures in cloud production environments. Responsibilities include model monitoring and optimization, responsible AI implementation, workflow automation, emerging technology evaluation, cross-functional architecture collaboration, code reviews, and mentoring. The role focuses on healthcare challenges and supports production-grade machine learning systems, including real-time inference and continuous integration and deployment.

Leads enterprise AI and machine learning initiatives by translating business problems into scalable solutions. Responsibilities include hands-on experimentation, exploratory and statistical analysis, feature engineering, Generative AI and agentic AI development, RAG architecture, proof-of-concept delivery, data visualization, documentation, and collaboration with business, product, architecture, engineering, and leadership teams to accelerate production adoption.

Lead the design, development, deployment, and technical direction of responsible Voice AI and machine learning capabilities. Mentor AI/ML engineers, establish engineering standards, and build scalable speech, transcription, translation, and multimodal solutions. Partner with business teams on architecture, cost estimation, roadmaps, vendor evaluation, and production delivery. Apply ethical AI, HIPAA-compliant security, cloud-native engineering, and LLM technologies to improve healthcare workflows and patient-agent experiences.