Applied AI Engineer Jobs

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The Forward Deployed Engineer Lead is responsible for partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. They architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. The role involves collaborating with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation. They own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, they shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer on Reflection's Applied AI team, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments, including public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. You will also contribute to evolving playbooks, processes, and best practices as part of a growing Forward Deployed Engineering organization.

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As a Senior Applied AI Engineer at Vanta, you will work cross-functionally to design and implement AI-powered features that deliver customer value and integrate large language models (LLMs) with Vanta's existing products and systems. You will collaborate with product engineers across Vanta to understand how AI systems can accelerate product adoption, instrument evaluations, guardrails, and monitoring, and review customer usage to continually improve quality. Additionally, you will collaborate with AI Platform engineers on foundational AI systems and tooling to accelerate product teams, make pragmatic tradeoffs considering business priorities, user experience, and sustainable technical foundation, mentor engineers, champion good technical and product instincts, and model a collaborative, high-ownership engineering culture.

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As a Forward Deployed Engineer Lead, you will own the end-to-end technical strategy, execution, and delivery of complex agentic applications, from early pre-sales discovery through production deployment. Responsibilities include partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. You will architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. Collaboration with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation is required. You will own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, you will shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer at Reflection, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments such as public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. Additionally, you will contribute to evolving playbooks, processes, and best practices as part of the growing Forward Deployed Engineering organization.

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AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems quickly. They build POCs, MVPs, and production integrations, and engage in executive-level conversations about architecture, strategy, and business outcomes. Responsibilities include shipping code, running benchmarks, debugging production issues, and architecting deployments. They lead discovery conversations, align stakeholders, and translate customer pain points into product improvements. Engineers work on building end-to-end POCs and MVPs inside customer codebases and infrastructure, architect inference foundations for GenAI core products, run load tests and tune deployments, deploy and validate new model families on inference frameworks, guide customers on model selection and fine-tuning strategies, build and run fine-tuning pipelines, and design evaluation frameworks. They manage customer engagement by leading discovery conversations, owning technical relationships, embedding with customer teams on-site, identifying recurring pain points, proposing product improvements, and codifying deployment patterns for internal use and platform improvement.

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As an Agentic AI/ML Engineer Intern, you will design and implement agentic workflows with tool use, memory, and orchestration to automate repetitive tasks and answer questions over internal and customer-facing data. You will contribute to AI Ops infrastructure including orchestration, evaluations, and observability, enabling agent-native DevOps to automate engineering and internal operations workflows. You will build and optimize RAG pipelines with vector databases and knowledge graphs to ground agents in the correct context. Additionally, you will set up evaluation pipelines to measure agent quality, reliability, and performance. This role involves prototyping, evaluating, and shipping agent-native solutions to multiply the impact of teams and technology, supporting scaling of customer base and operations without scaling headcount linearly.

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Design and build agentic workflows that leverage tool use, memory, planning, and orchestration to automate repetitive tasks and enable natural-language access to internal and customer-facing data. Contribute to FieldAI's AI Ops platform by developing agent infrastructure for orchestration, evaluation, observability, and reliability, and apply these capabilities to create agent-native DevOps workflows that automate engineering, support, and operational processes. Develop and optimize retrieval systems, including RAG pipelines, vector databases, and knowledge graph integrations, to provide agents with accurate, relevant, and scalable context. Build evaluation frameworks and automated testing pipelines to measure agent quality, reliability, safety, latency, and business impact, using those insights to continuously improve system performance. Prototype, iterate, and deploy AI-powered tools that improve internal productivity and deliver actionable insights to customers. Partner closely with engineering, product, field operations, and customer-facing teams to identify high-leverage opportunities for automation and agent-driven workflows.

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Design agent systems from first principles including deciding the loop, tools, context strategy, evaluation harness, and system topology. Engineer the context by focusing on prompt construction, context windows, tool surfaces, structured outputs, and citation grounding. Drive evaluation rigor by building evaluations prior to agent construction, diagnosing failures, fixing root causes, and proving improvements through metrics. Use AI tooling such as Claude Code and Codex extensively to plan, scaffold, refactor, and debug work. Become a domain expert in healthcare claims, coding guidelines, and medical records as an integral part of the job.

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As a Senior Backend & AI Engineer, the responsibilities include designing, developing, deploying, and operating business-critical AI features. The role involves collaborating on requirements analysis to design technical and business solutions, proposing innovative solutions by staying ahead of AI trends and technologies, owning key responsibilities in the design, architecture, and end-to-end delivery of AI-driven modules, and writing clean, scalable, and maintainable code with proper testing, deployment, and monitoring. Additional duties include continuously improving code quality by refactoring, debugging, and enhancing performance, contributing to building secure, high-quality AI solutions for customer experience, optimizing product and platform performance with live site monitoring, and participating in an on-call rotation to handle critical incidents and maintain system uptime.