Top Artificial Intelligence Jobs

Own customer engagements from discovery through deployment, adoption, and measurable business impact. Translate complex industrial workflows into MVPs, data requirements, user stories, and AI-powered solutions with engineering. Manage tradeoffs between urgency, technical complexity, commercial value, and product reusability. Lead customer rollout, identify repeatable patterns, support expansion and renewal, and turn customer-specific learning into scalable product and delivery processes.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Own and scale the software platform connecting IoT hardware, AI models, cloud infrastructure, APIs, dashboards, and firmware integrations. Design distributed architectures, build reliable backend services, operate production systems, and improve observability, cost efficiency, and scalability. Collaborate with scientists, hardware engineers, product, and deployment teams while mentoring colleagues on safe AI-augmented development workflows. Contribute to technical strategy and deliver quickly in a startup environment.

Lead ONDC integration by designing and implementing backend services and APIs. Ensure compliance with ONDC/Beckn standards, handle data modeling, performance tuning, API security, and transaction flows. Integrate logistics, payments, and catalog systems, write tests, conduct code reviews, and maintain high availability for ONDC services.

Provides onsite HR support for Las Vegas pharmacy and PBM operations. Manages onboarding, recruiting, employee relations, benefits inquiries, HR initiatives, compliance, HRIS records, manager coaching, engagement, retention, and local workforce reporting across multiple sites.

Own post-sale relationships for law firm and corporate IP customers from onboarding through renewal and expansion. Lead tailored training, build success plans, monitor account health, drive adoption and ROI, identify expansion opportunities, and coordinate escalations. Serve as the customer voice to Product and Engineering while helping build scalable customer success playbooks, materials, health scoring, and processes.

Build and maintain a large case law dataset through document metadata pipelines, web scraper reliability systems, citation-resolution utilities, APIs, and evaluation benchmarks. The role requires backend technical generalism, data pipeline expertise, and basic-to-intermediate infrastructure and DevOps skills. Python and SQL are core, while ML and AI experience is beneficial. This is an in-person position in SoHo, New York City.

Build production software and scalable cloud systems for AI-powered insurance calls. Responsibilities include designing core LLM systems, refining prompts, developing privacy- and security-conscious solutions, orchestrating thousands of concurrent phone calls, integrating FHIR APIs, testing, debugging, and documenting software. The role also contributes to product direction, engineering practices, and company culture as a foundational team member.

Own the workflow engine powering customer runs, including scheduling, queues, retries, batching, idempotency, provider integrations, observability, and cost controls. Scale Postgres and Redis while improving reliability across high-volume data workflows. Work autonomously, use coding agents extensively, and make technical decisions for an early-stage platform.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.