Top AI & Machine Learning Jobs

Design, build, and scale enterprise AI-powered platforms using GenAI, LLMs, RAG, embeddings, and NLP pipelines. Architect distributed, microservices-based, event-driven systems and production-grade APIs using Go or Python, cloud platforms, databases, and containerized deployments. Lead system design, secure engineering, CI/CD, observability, reliability, and troubleshooting. Influence technical direction across teams, mentor engineers, collaborate with stakeholders, and drive end-to-end delivery of scalable AI solutions.

Designs and delivers enterprise AI and machine learning solutions, including generative AI, fine-tuned LLMs, RAG, agentic systems, Document AI, OCR, and model deployment. Partners directly with business stakeholders to gather requirements, architect solutions, and own end-to-end delivery. Applies supervised, unsupervised, reinforcement learning, deep learning, and NLP techniques using Python, PyTorch, cloud platforms, APIs, and containerized environments. Healthcare experience is preferred.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Design, develop, and maintain enterprise generative AI and agentic AI applications using Python, LangGraph, LangChain, and Azure services. Build RAG solutions, multi-agent systems, backend APIs, and scalable microservices. Deploy cloud-native applications, establish CI/CD and infrastructure-as-code practices, and optimize production systems for performance, reliability, scalability, security, observability, and cost efficiency.

Develop and lead production AI/ML solutions for healthcare, including NLP, deep learning, generative AI, forecasting, and computer vision. Manage the full machine learning lifecycle from data preparation and feature engineering through deployment, monitoring, and documentation. Optimize and quantize generative AI models, build proof-of-concepts, establish development best practices, and collaborate with research, engineering, and product teams. Promote ethical AI through fairness, transparency, and accountability.

Design, deploy, and maintain scalable machine learning and Generative AI solutions for healthcare and business use cases. Build predictive models, NLP, deep learning, LLM, RAG, vector search, data pipelines, and MLOps platforms. Optimize production AI systems for accuracy, performance, cost, security, and reliability while meeting governance and HIPAA requirements. Provide technical leadership, mentor engineers, evaluate emerging AI technologies, and collaborate with technical and business stakeholders.

Develop and deploy advanced AI, machine learning, deep learning, NLP, and computer vision solutions for healthcare. Lead end-to-end model development, including data preparation, feature engineering, modeling, deployment, monitoring, documentation, and lifecycle management. Optimize generative AI models, apply RAG and agentic AI techniques, conduct complex proofs of concept, establish development best practices, and collaborate with research, engineering, and product teams while promoting ethical AI.

Technically lead distributed teams while designing, building, deploying, and supporting scalable AI and machine learning applications. Responsibilities include architecture and design reviews, application ownership, performance optimization, stakeholder engagement, model evaluation, exploratory data analysis, deep learning implementation, agentic architecture design, prompt engineering, and CI/CD delivery using cloud and modern development tools.

Lead the architecture, development, deployment, optimization, and production support of scalable cloud-native Generative AI and Agentic AI platforms. Design multi-agent systems and high-performance backend APIs, operate mission-critical AI workloads, improve reliability and performance, ensure responsible and compliant AI practices, and mentor junior engineers through technical guidance and design reviews.

Leads AI innovation and adoption across a healthcare advisory practice. Develops and deploys predictive models, generative AI, machine learning, and analytics solutions for value-based care and payment challenges. Oversees data readiness, governance, responsible AI reviews, model monitoring, stakeholder adoption, and client delivery. Translates complex analytics into executive-ready insights, dashboards, and presentations while building reusable assets, training teams, and driving enterprise AI strategy.