Top AI & Machine Learning Jobs

Lead design and implementation of AI Ops solutions (RAG, agentic workflows) with enterprise reliability and responsible AI. Improve SRE practices, lead incident response, automate infrastructure with Terraform and GitHub Actions, operate Kubernetes clusters, and develop software using Python and Node.js while mentoring globally distributed teams.

Lead enterprise Generative AI and Agentic AI architecture, platform development, foundation-model selection and fine-tuning, RAG and vector retrieval, multi-agent workflows, AI observability, evaluation, governance, security, and cost optimization. Build scalable AI services across cloud providers, establish reusable frameworks and standards, and partner with engineering, product, security, architecture, and business leaders. Provide technical leadership and mentor AI practitioners while operationalizing reliable, compliant AI solutions.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

Lead multiple engineering teams to deliver enterprise conversational AI (voice, IVR, chat) on AWS. Drive architecture, cloud-native platform engineering, AI/ML operationalization, quality engineering, program delivery, and organizational growth while ensuring reliability, scalability, and measurable business outcomes.

Lead hands-on experimentation and prototyping of Generative and Agentic AI solutions (LLMs, RAG, vector retrieval). Build production-ready POCs, reusable prompts, architectures, and implementation assets; enable engineering adoption; apply AI development lifecycle practices, evaluation, cost optimization, and governance; measure POC-to-production adoption and business impact.

Design, build, deploy, and operate AI/ML and Generative AI solutions using AIDLC practices. Build data pipelines, feature engineering, model training/evaluation, APIs and microservices, cloud-native deployments, MLOps/LLMOps, monitoring, and agentic AI capabilities (LLMs, RAG, embeddings). Collaborate with data, platform, and product teams and ensure governance, security, and responsible AI practices.

Lead architecture and technical strategy for GenAI and Amazon Connect implementations. Translate business requirements into scalable conversational AI solutions, design integrations across CRM/WFM/data layers, validate development outputs, support testing/UAT, and promote reuse, security, and performance across cloud-based GenAI and digital routing systems.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Lead design and delivery of scalable, secure cloud-native AI platforms and agentic AI solutions. Own end-to-end AI service lifecycle, optimize backend performance, deploy mission-critical AI workloads, collaborate with product/security/governance teams, and mentor junior engineers.

Design, build, and deploy AI/ML and generative AI solutions and full-stack applications. Develop frontend experiences, backend services, microservices, APIs, and cloud-native data integrations. Implement RAG, LLMs, vector DBs, dashboards, reporting, and self-service analytics to support quality, patient safety, and workflow automation. Provide technical leadership, mentor engineers, and apply CI/CD, testing, observability, security, and performance best practices.