Top AI Engineer Jobs

Design, build, and scale a multi-tenant AI Ops platform: develop high-throughput microservices, GraphQL/REST APIs, backend infrastructure, data systems, telemetry, and CI/CD deployments for operationalized ML/AI services.

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 applications; evaluate retrieval, tool-use, orchestration, and model-performance approaches; and ensure reliability, security, testing, observability, and maintainability. Partner with technical and business stakeholders to identify workflow improvements, influence architecture and engineering standards, support delivery planning, and mentor less experienced engineers.

Design, build, and deploy enterprise-grade autonomous AI services and multi-agent systems. Implement MCP and A2A integrations, architect hybrid control flows and state machines, integrate sandboxed tool execution with strict egress controls, and apply SRE principles for observability, fallback mechanisms, and reliability to move generative AI from POC to production.

Design, build, and operate secure LLM platforms and RAG pipelines for security analytics and automation. Develop agentic workflows, continuous monitoring, governance, and adversarial testing to mitigate AI risks and integrate protections into security tooling and cloud-native ML pipelines.

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.

Build production-grade AI systems and multi-agent workflows that automate B2B trade processes, including collections, credit decisioning, payments operations, and other manual workflows. Develop reliable LLM-powered features with evaluations and guardrails, write clean performant code, collaborate with platform, product, and design teams, and help shape AI product and engineering practices in an early-stage environment.

Develop and enhance enterprise AI platforms and operational services, onboard internal teams, and build production-ready generative and agentic AI solutions using Python and AWS. Lead AI development initiatives, select appropriate AWS AI services, support AI governance, and promote enterprise AI adoption. Collaborate with engineering teams and stakeholders to deliver secure, effective solutions while applying software engineering and lifecycle best practices.

Lead AI products end to end, from prototyping and evaluation through deployment, monitoring, and iteration. Own matching, search, ranking, embeddings, and LLM-powered features while shipping production code across Python services, Rails, and React. Develop evaluation systems for nondeterministic AI, analyze product data with SQL, run experiments, review code, and deliver measurable improvements to healthcare career marketplace products.

Design and develop scalable AI platforms and developer tools using Python, MLOps, LLM prompting, and agentic workflows. Build ETL and data pipelines, integrate and deploy models on cloud infrastructure, implement monitoring and reliability dashboards, prototype generative AI applications, and establish testing, documentation, security, and ethical AI standards. Collaborate with data, engineering, and product teams to operationalize intelligent applications.

Develop and optimize video processing applications and algorithms for desktop and mobile. Research and evaluate video processing and neural-rendering methods, implement modular, high-performance code, integrate and deploy deep learning models across Mac, Windows, iOS and Android, and build test environments and tools for runtime verification.

Leads the architecture, development, and production deployment of AI agents and cloud-native platforms on GCP. Builds solutions with LangChain, LangGraph, RAG, prompt and context engineering, MLOps, observability, and scalable microservices. Oversees engineering metrics, DevSecOps, FinOps, troubleshooting, technical strategy, documentation, agile delivery, and stakeholder presentations. Manages and mentors cross-functional software, quality, and reliability engineering teams while driving reliable, efficient, production-ready AI systems.

Design, test, optimize, deploy, and operationalize AI/ML models across cloud, edge, and hybrid environments. Lead architecture selection, deployment pipelines, MLOps workflows, feature engineering, fine-tuning, retraining, and model monitoring. Collaborate with data, cloud, network, and security teams to integrate reliable models with mission systems, detect drift and performance degradation, and guide AI/ML lifecycle strategy for mission-critical applications.

Design, build, test, and deploy Retrieval-Augmented Generation (RAG) and agentic AI systems. Perform prompt engineering, data preprocessing, feature engineering, model evaluation and tuning. Integrate and optimize AI solutions on AWS (SageMaker, Bedrock, Lambda, S3), write maintainable Python code, troubleshoot in a mono-repo, and document models and workflows while collaborating cross-functionally.

Lead AI Engineer driving AI-assisted engineering adoption, defining reference architectures, conducting reviews, building LLM-powered agents and developer tools, improving CI/CD and security practices, mentoring engineers, and enabling cross-team engineering excellence across Output Solutions.

Lead AI Engineer responsible for establishing engineering standards, architecture patterns, CI/CD and security frameworks, championing AI-assisted development, building LLM-powered agents, mentoring senior engineers, and driving quality, reliability, and delivery improvements across Output Solutions.

Leads the architecture, development, deployment, and optimization of agentic AI applications and autonomous systems. Builds Python backend services, secure APIs, RAG pipelines, microservices, and enterprise integrations using GenAI platforms and LLM frameworks. Establishes CI/CD, testing, evaluation, cloud-native deployment, resilience, and security practices. Provides technical thought leadership, mentors engineers, troubleshoots vulnerabilities and software issues, and manages technology risks and compliance.

Design, build, and deploy production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through monitoring and iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and best practices.

Leads full-stack software engineering for JPMorganChase’s Wholesale Credit Risk technology team. Designs, develops, tests, troubleshoots, and reviews secure production code; improves application resilience and operational stability; promotes responsible AI-assisted engineering practices; automates recurring remediation; and evaluates architectures and technologies with vendors and internal teams. The role requires cloud-native development, agile SDLC expertise, financial services knowledge, data architecture, modern front-end technologies, GraphQL, and API development.

Develop AI and data solutions for aerospace engineering, including machine learning systems, proof-of-concepts, distributed data architectures, technical code, and production applications. Interns collaborate cross-functionally, present architectures and demos, apply cloud and database technologies, and use methods such as Game Theory and DecisionIQ to deliver business value. The role is remote and offers mentorship, project ownership, and potential return offers.

Individual contributor lead role to architect, build, and deploy agentic LLM systems, RAG/hybrid search, and scalable microservices on cloud/Big Data platforms. Owns MLOps/LLMOps, evaluation frameworks, HIPAA-compliant security controls, and integrations with data and internal AI platforms. Partners with data engineering, mentors engineers, prototypes POCs, and drives production reliability and cost KPIs.

Design and build AI agents, first-party web and mobile apps, and scalable backend services. Integrate LLM frameworks and enterprise systems, implement APIs and microservices, ensure observability and security, and collaborate with Finance, Security, and Engineering stakeholders.

Lead development of secure, scalable Java full-stack applications for mortgage technology. Design, develop, test, troubleshoot, review, and debug production code; improve operational stability through automation and remediation. Drive responsible adoption of enterprise-approved AI-assisted development tools, establish validation and security standards, coach engineers, and evaluate vendor and architectural solutions. Collaborate within agile teams using SDLC, CI/CD, resiliency, and security practices.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

Design and build production-grade GenAI solutions: RAG pipelines, agentic multi-agent workflows, knowledge-base chatbots, vector DBs, LLM integrations, observability, PII redaction, and deploy AI backends with monitoring and documentation.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.