Top AI & Machine Learning Jobs

Design and implement backend orchestration for agent workflows and runtime in microservices. Build scalable LLM-powered systems (RAG, tool-use, multi-step reasoning), integrate LLM/TTS/ASR, develop agents with frameworks like LangChain/Dify/OpenAI SDK, optimize prompts, run integration tests, and collaborate with research and product teams to deploy agentic AI across verticals.

Leads AI-focused technical pre-sales, client discovery, solution architecture, and commercial planning. Designs scalable AI platforms across AWS, Azure, GCP, Snowflake, and Databricks; operationalizes LLMs and agentic workflows; and guides enterprise AI initiatives from pilots to production. Develops proposals, reference architectures, accelerators, thought leadership, and executive-ready ROI narratives while partnering with sales, delivery teams, and technology partners.

Lead development of Allswell’s AI-powered mental health product suite, building full-stack experiences for patients, providers, and clinicians. Serve as the team’s most senior engineer, contributing hands-on across backend APIs, frontend interfaces, AI systems, and AWS infrastructure. Establish technical direction, mentor engineers, collaborate with product and clinical teams, and help grow the engineering organization while delivering scalable 0-to-1 products.

Develop and optimize industrial-grade machine learning and computer vision software for biological imaging. Build algorithms for image analysis, object detection, and segmentation; optimize performance using GPU acceleration and parallel processing; and translate research prototypes into reliable production systems. Collaborate with data scientists, bioinformaticians, and domain experts while supporting regulatory compliance, documentation, testing, code reviews, and agile development.

Directs enterprise AI engineering strategy, architecture, implementation, operationalization, governance, and continuous improvement. Leads generative AI, RAG, agentic workflow, MLOps, and LLMOps initiatives; oversees integrations, cybersecurity and regulatory controls, vendors, costs, metrics, and solution delivery. Builds the AI engineering capability through team development, mentoring, innovation, and technical standards in a regulated life sciences environment.

Leads Verizon’s network automation and infrastructure AI organization, driving autonomous, self-healing operations across virtualized RAN and 5G Core. Oversees cloud-native orchestration, closed-loop assurance, AI/ML optimization, energy management, policy-as-code, and on-premises AI inference at national scale. Builds distributed engineering, SRE, MLOps, and network automation teams; establishes autonomy standards and roadmap toward TM Forum Level 4. Partners with network intelligence teams, standards bodies, hyperscalers, and executive stakeholders while ensuring production security, resilience, and compliance.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Leads end-to-end AI enablement consulting engagements, including readiness assessments, governance frameworks, champion programs, workshops, use-case discovery, adoption change management, and training. Develops executive-ready strategies, roadmaps, risk models, dashboards, and other client deliverables. Advises senior AI leaders, mentors junior staff, supports AI solution deployment, evaluates emerging tools, and contributes to business development and revenue growth.

Build and integrate machine learning systems using real-world sensor, document, and operational data. Responsibilities include developing Python data pipelines, training and evaluating models, integrating models or LLM workflows into tools, investigating failure modes, documenting results, and using version control, testing, code review, and experiment tracking. The intern will own a scoped project and present findings and recommendations at the end of the summer.

Designs and deploys production-ready AI solutions for strategic enterprise customers using Genesys Cloud, agentic AI, conversational AI, automation, data, and integrations. Leads discovery, architecture, prototyping, deployment, optimization, governance, and value realization. Advises executive stakeholders, connects technical decisions to business KPIs, develops reference architectures, guides responsible AI practices, and builds customer capability across complex enterprise environments.