Top AI Engineer Jobs

Build and deploy AI-powered automations, tools, agents, dashboards, data pipelines, and agentic workflows for a private equity firm and its portfolio companies. Evaluate emerging AI models and frameworks, rapidly prototype solutions, integrate Claude and Microsoft 365 tools, and iterate based on user feedback. The role requires shipping working code, collaborating directly with investment professionals, and working onsite in Manhattan Beach five days per week.

Build and deploy AI-powered automations, tools, agents, dashboards, data pipelines, and agentic workflows for a private equity firm and its portfolio companies. Evaluate emerging AI models and frameworks, rapidly prototype solutions, and iterate based on user feedback. The role requires strong practical AI tool usage, coding ability in Python or JavaScript, Microsoft 365 proficiency, and five-day in-office work in Manhattan Beach.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

Leads the design, development, deployment, and operationalization of enterprise AI/ML, generative AI, and automation solutions. Provides technical leadership across architecture, engineering practices, evaluation, monitoring, governance, security, and responsible AI. Partners with cross-functional stakeholders, mentors engineers, establishes reusable standards, evaluates production readiness, and ensures scalable, reliable, compliant, and cost-effective delivery of AI/ML capabilities.

Build scalable backend services, distributed systems, APIs, and platform infrastructure for production AI and agentic applications. Develop reusable agent frameworks, evaluation pipelines, SDKs, and Python libraries while improving CI/CD, testing, observability, security, reliability, and cost efficiency. Use Snowflake’s AI ecosystem to deliver intelligent internal products, collaborate with cross-functional stakeholders, and establish engineering best practices for enterprise AI systems.

Develop and deploy AI solutions from problem framing through production. Responsibilities include identifying AI opportunities, leading MVP development, building data pipelines, experimenting with generative LLMs, fine-tuning NLP models, and delivering scalable features. The intern will collaborate cross-functionally, communicate technical impact to business audiences, and work with cloud platforms such as AWS or Google Cloud.

Owns end-to-end product surfaces and production AI features, including LLM applications, prompt and evaluation pipelines, retrieval, agent workflows, reliability, and full-stack web and mobile development. The role requires substantial ownership, production AI experience, launch resilience, and technical leadership in a fast-paced, in-person environment.

Build scalable full-stack applications integrating AI and LLM technologies into web and mobile learning products. Responsibilities include experimenting with emerging AI tools, developing AI workflows, optimizing LLM applications, and collaborating with product, design, sales, and marketing teams. The role requires expert frontend, backend, database, cloud, infrastructure, and React Native experience, plus 5+ years of experience and startup or founding-engineer background.

Build production AI and optimization systems for logistics allocation and packing. Develop heuristics and exact methods, encode physical constraints, maintain data inputs, integrate outputs into routing and resupply workflows, and validate solutions for feasibility, performance, and explainability. The role requires strong Python or equivalent programming, operations research or applied mathematics expertise, and experience with packing, allocation, optimization, and large-scale operational systems. U.S. citizenship and ability to obtain a government security clearance are required.

Design, build, test, deploy, and support governed data products and pipelines for supply chain operations. Develop Snowflake and dbt models, optimize workloads, implement data quality and observability controls, and use Databricks and Spark for scalable analytics and AI-ready data foundations. Partner with product, engineering, analytics, and business teams to define requirements and data contracts, lead initiatives through production, document solutions, and improve reliability, security, and engineering standards.

AI Enablement Engineer who helps non-engineering Builders turn AI prototypes into secure, scalable, tested, observable, and maintainable production applications. Responsibilities include architecture guidance, technical coaching, code and security reviews, deployment, reusable templates and guardrails, production support models, and occasional end-to-end strategic builds. The role partners with engineering, security, and data teams while advancing AI application standards and Builder self-sufficiency.

Design and deliver production AI applications from concept through launch, including agent-based systems, retrieval, orchestration, tool integrations, and evaluation. Partner with product and design teams, build lean initial products, improve production readiness, and develop reusable components. The role requires strong communication, comfort with ambiguity, entrepreneurial initiative, and hands-on Python engineering experience deploying AI products used by real customers.

Build and operate AWS-based data platforms, Java/Spring Boot services, reliable data pipelines, and AI-powered applications. Responsibilities include data ingestion and modeling, predictive ML, generative AI and RAG with Bedrock and OpenSearch, production monitoring, security, CI/CD, infrastructure as code, and integrations with databases, POS, payment, and partner systems.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Design, deploy, and scale production AI systems and applications. Responsibilities include building data pipelines, tuning and hardening models, collaborating with researchers, engineers, and product managers, monitoring live models, tracking AI advances, conducting code reviews, and owning solutions end to end amid changing priorities.

Research and develop novel AI models, algorithms, and systems; validate them through rigorous experiments and large-scale distributed training; and transition prototypes into production. The role involves deep learning, LLM pre-training and alignment, efficient GPU/TPU utilization, scientific analysis, technical writing, mentoring, code and paper reviews, open-source contributions, and end-to-end project ownership.

Develop ABAP integrations and AI agents for complex SAP environments. Build software across the frontend, backend, infrastructure, and AI stack; design specialized LLM harnesses; support enterprise customers; identify SAP implementation edge cases; and influence product direction. The role requires strong ABAP and SAP expertise, experience with large implementations and migrations, and pragmatic production engineering skills.

Design, build, train, and deploy AI agents and machine learning systems for complex technical data. Develop context handling, indexing, search, and information retrieval workflows; integrate AI systems with enterprise platforms; and ship reliable production features. Collaborate with customers and engineers, prototype rapidly, influence architecture and product direction, and help establish engineering culture as an early engineering hire.

Build AI-native developer tools, spec-to-implementation workflows, code-generation systems, internal copilots, coding harnesses, sandboxes, authorization layers, and verification guardrails. Develop customer-facing AI functionality within a data-heavy enterprise SaaS platform, collaborate across engineering, product, and operations, and coach teams on safe AI-assisted development practices.

Design and build production AI agent systems that orchestrate large language models, tools, and deterministic code. Develop evaluation suites, improve model behavior, diagnose production failures, and create feedback systems for training and evaluation. Extend browser and computer-use agents with strong safety, security, and auditability. The role requires end-to-end ownership, robust systems design, and on-site collaboration five days per week.

Build and operate Python backend services and AWS infrastructure for autonomous AI identity workers. Responsibilities include durable workflow execution, multi-tenant security, PostgreSQL and vector retrieval, model API integrations, analytics pipelines, usage reconciliation, infrastructure as code, observability, deployment controls, auditability, and recovery testing. The Staff engineer will lead a domain such as execution, tenant security, data infrastructure, or reliability across teams.

Leads a team of cloud and AI solution engineers supporting customer transformation initiatives. Coaches team members on technical sales, manages customer and stakeholder relationships, develops pipeline, coordinates partners, and drives cloud, data, AI, and application opportunities. Evaluates customer applications, recommends Azure and other cloud solutions, demonstrates technologies, supports technical decisions, and builds team expertise in cloud migration, modernization, and competitive platforms.

Designs and builds scalable model and agent runtimes across hybrid-cloud environments. The role focuses on AI platform architecture, multi-tenant infrastructure, inference optimization, RAG, observability, automated evaluation, governance, and operational reliability. It requires strong software engineering experience, preferably in Python and Kubernetes, plus expertise in distributed systems, cloud services, and production-grade AI/ML platforms.

Supports an AI-powered coding assistant by enabling model services, configuring AWS IAM policies, supporting vendor platforms, and providing T1/T2 IT helpdesk assistance. The role also delivers application support, change management, architecture and design assistance, documentation, stakeholder communication, and troubleshooting of AI-generated code and developer workflows.

Develop and maintain full-stack applications, including front-end interfaces, back-end services, REST APIs, Oracle database solutions, and AI-assisted development workflows. Participate in requirements analysis, design, testing, deployment, troubleshooting, security, and operational support. Collaborate with technology teams and business users, apply Agile practices, assess risks, and mentor junior analysts while operating with substantial autonomy.