Top AI & Machine Learning Jobs

Design and scale a unified AI/ML data collection platform supporting data pipelines, LLM and RAG workflows, model training, evaluation, deployment, observability, and lifecycle management. Build production-grade distributed and cloud-native systems, implement MLOps practices, monitor model performance and reliability, optimize costs, and mentor engineers while collaborating with product, research, engineering, and business teams.

Build and operate production AI applications and shared platform capabilities using AWS Bedrock and Python. Develop AI agents, MCP servers, APIs, and distributed services; integrate AI into existing workflows; evaluate retrieval, tool-use, and orchestration approaches; and improve reliability, security, observability, and maintainability. Partner with technical and business stakeholders to identify opportunities, guide technical decisions, support delivery planning, and mentor less experienced engineers.

Lead AI use-case discovery, design, prototyping and delivery. Partner with business leaders to frame outcomes, design architectures (RAG, LLMs, cloud AI), build MVPs using Python/APIs and low-code tools, and drive adoption with governance and responsible AI practices.

Build and own end-to-end NLP and ML solutions (including LLMs, RAG, embeddings) to extract structured information from documents. Responsibilities include problem definition, data exploration, model development, evaluation, productionization, monitoring, and cross-team collaboration to improve data quality and coverage.

Build and iterate fast AI prototypes for advisor workflows, integrating models, signals, APIs, and UX. Test feasibility and performance, capture technical learnings, and produce handoff documentation for production teams.

Design, build, and scale production GenAI services (MCP Server/Gateway, AI Agent). Own end-to-end features, ensure reliability, performance, and security, use AI-assisted development tools, collaborate with product/design, and mentor teammates while producing documentation and monitoring systems.

Lead design and launch of a tiered AI partner marketing program. Execute co-marketing, sales enablement, in-platform brand governance, attribution and reporting, client education, and cross-functional operations to drive partner-sourced pipeline and $1M closed-won revenue in year one.

Architect and lead development of scalable AI and cloud-native agentic solutions and AI transcription platforms using OpenAI, Azure, and AWS. Design, build, and maintain high-performance distributed systems; provide technical leadership, enforce engineering best practices, lead DevOps (Docker, Kubernetes, AWS Fargate) and CI/CD with SonarQube, optimize infrastructure costs, and collaborate cross-functionally to align technical solutions with financial business needs.

Design, build, and operationalize a unified AI/ML data collection platform. Responsibilities include LLM/RAG pipelines, data ingestion, feature management, model lifecycle and deployment, observability, scalable distributed systems, and mentoring engineers to deliver production-ready ML solutions.

Rapidly prototype AI solutions embedded in advisor workflows: integrate models, signals, APIs and UX into experiments, validate feasibility and performance, and hand off documentation to production teams.