Top AI Engineer Jobs

Build and ship production-grade agentic AI systems and LLM-powered features for financial workflows. Design RAG pipelines over sensitive data, contribute to shared AI infrastructure (LLM services, orchestration, monitoring), ensure reliability, security, and auditability, and collaborate with product and design while owning systems in production.

Design, build, and deploy production-grade Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and swapping, model fine-tuning and quantization, scalable FastAPI backends, containerized deployment, and monitoring/optimization for latency, cost, and reliability.

The Solutions Architect will partner with startups to develop and scale Generative AI applications, providing technical guidance and prototyping support, while synthesizing feedback for product improvement.

Build production-grade AI agents and RAG pipelines that integrate with internal tools (BookStack, Slack, Asana, Gmail). Create REST APIs, ingestion pipelines, and function-calling workflows; monitor and iterate on agent performance while collaborating with backend (.NET) and frontend (Angular) teams.

Build and operate signal-based GTM activation workflows across Common Room, Salesforce, and sequencing platforms. Design enrichment waterfalls, translate audiences into sequences and SDR tasks, partner with Revenue Technology on Salesforce changes, and instrument dashboards, QA checks, and experiments to improve conversion and speed.

Architect and own core capabilities of an internal AI agent platform: design deployment patterns, shared services, observability, integrations, and developer tooling; partner with stakeholders to turn strategic goals into production-ready agent solutions; troubleshoot and unblock builders while improving platform reliability, scalability, security, and reuse.

Build and own a space-based AI marketplace from MVP to scale, including the storefront, partner portal, admin tools, APIs, onboarding, entitlements, metering, integrations, deployment workflows, and third-party connectors. The role requires end-to-end full-stack development, cloud-native infrastructure, testing, CI/CD, monitoring, secure multi-tenant authorization, and collaboration with engineering teams. The founding developer will ship weekly on a small team and use AI-assisted development workflows.

Build and own a space-based AI marketplace from MVP to scale, including its storefront, partner portal, admin tools, APIs, onboarding, entitlements, metering, integrations, deployment workflows, and third-party connectors. The role requires full-stack development, cloud-native infrastructure, multi-tenant authorization, testing, CI/CD, monitoring, and close collaboration with engineering teams.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Designs, trains, evaluates, and deploys ML and AI models; implements MLOps for production, monitors model performance, collaborates with data scientists and stakeholders, and optimizes scalability and reliability.

Build and scale LLM-powered analytics and retrieval systems for healthcare data. Implement RAG, text-to-SQL, retrieval pipelines, evaluation/guardrails, and compliance controls. Integrate tools/data sources, deploy on AWS, monitor model performance, and collaborate with product and senior engineers to deliver production features.

Lead end-to-end design, build, and deployment of production LLM/AI systems that convert unstructured life-sciences data into ontologies and actionable commercial insights. Collaborate with founders and engineering to define product direction, system architecture, and scalable infrastructure, then iterate to improve performance, reliability, and user experience.

Support design, build, and deploy AI/ML models and AI-powered HR features (agents, chatbots, recommendations). Collaborate with HR, IT, and data teams, build HR data pipelines, document workflows, and handle sensitive HR data with confidentiality.

Work with enterprise customers to identify problems, design and ship LLM-powered and agentic systems to production. Build context-learning capabilities, knowledge-management agents, evaluation and confidence systems, and scalable AI orchestration to drive adoption and expand impact.

Design, build, and deploy LLM-powered production systems (agents, RAG pipelines, voice agents). Integrate with enterprise stacks (CRMs, ERPs, data warehouses), harden with evals, observability, and guardrails, run client scoping and delivery, and feed learnings into internal tooling and playbooks.

Lead design and deployment of production AI systems: architect model serving, inference, evaluation, and optimization pipelines; improve latency, throughput, quality, and cost; lead cross-team AI initiatives; set engineering standards; mentor engineers; partner on AI strategy and evaluate emerging models and infrastructure.

Embed as the senior technical lead inside portfolio companies to discover business problems, architect and ship production AI systems (model/provider selection, retrieval/grounding, guardrails), own reliability and deployment end-to-end, mentor engineers, and convert solutions into reusable patterns and platform assets.

Own and improve backend services for an AI-native learning platform: build Python/Node.js services, design serverless/cloud-native architectures, implement event-driven workflows and advanced NoSQL models, integrate LLM-based features (RAG, embeddings, vector search), ensure production reliability, observability, testing, and frontend API integrations while collaborating in a cross-functional remote team.

Optimize distributed training for 100B+ models across massive GPU fleets by writing and tuning custom kernels, improving IO/checkpointing/fault tolerance, building monitoring tooling, co-designing model and training recipes, and benchmarking heterogeneous accelerators and parallelism strategies.

Lead end-to-end ML implementations for clients, design and maintain production-grade MLOps pipelines, deploy and optimize Generative AI/LLM and NLP solutions, manage infrastructure (Docker, orchestration, distributed compute), and communicate technical results to stakeholders.

As a Machine Learning Engineer, you will build agentic systems for autonomous web navigation and complex task completion, integrating LLMs and reinforcing learning.

The AI Engineer will design and build core agent systems, integrating them with tools and ensuring compliance, while optimizing performance and collaborating with scientists and engineers.

The Senior Technical Data Product Manager will oversee the data product roadmap, collaborating with data engineering and architecture teams. Responsibilities include assessing legacy systems, developing product roadmaps, driving cross-functional deliveries, and ensuring technical requirements are met, aiming for swift execution and measurable impact.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.