Top AI Engineer Jobs

Design and build AI-enabled automation tools, APIs, scripts, and workflows to streamline Verification & Validation processes. Implement generative AI use cases, integrate solutions with engineering systems and CI/CD pipelines, run pilots and transition to production, and collaborate with global teams to drive adoption and improve engineering efficiency.

Own technical onboarding and go-live for Voice AI deployments: number provisioning, telephony configuration, porting, SIP/SBC debugging, production escalations, and partner with Product on conversational call flows while documenting compliance and repeatable processes.

Review, secure, and productionize AI-generated prototypes into scalable, cloud-native applications. Guide architecture and solution design, partner with Security to remediate vulnerabilities, build governance and standards, and support deployments and handoffs to Product and Engineering teams.

Design, develop, train, validate, and deploy AI/ML products for life-sciences customers. Advise leadership on technical and commercial feasibility, collaborate with cross-functional and commercial teams, and lead projects in RL, LLM agent harnesses, fine-tuning, governance, and model evaluation.

Design, build, and maintain Trustly's internal AI platform and agentic workflow tooling. Partner with product and engineering teams to embed LLM-powered capabilities, lead prototyping and model evaluations, establish evaluation standards for quality and safety, and create reusable libraries, patterns, and enablement resources to drive internal AI adoption.

Design, configure, and maintain enterprise AI governance platforms and workflows (evidence capture, controls, reporting). Build integrations and automations with enterprise systems, enable metrics and data quality, translate Responsible AI policies into operational controls, coordinate vendors, and deliver training and enablement to drive adoption and audit readiness.

Lead design and implementation of AI-ready data platforms and scalable batch/streaming pipelines to support Generative AI and RAG solutions. Own architecture, reliability, governance, observability, and operationalization of production GenAI data systems across cloud environments. Mentor engineers and collaborate cross-functionally to integrate AI data products into enterprise workflows.

The Senior Software Engineer will develop a robust AI infrastructure, focusing on backend services, APIs, and AI agent optimization. Responsibilities include architecture design, evaluation systems, and collaboration with cross-functional teams.

Design and implement core features across the stack (Python, TypeScript, Rust); make product-focused engineering decisions; build scalable, well-tested, documented production software; prioritize engineering investments; collaborate in a remote synchronous team; contribute to consumer apps, developer platforms, infrastructure, or AI research.

The Senior Software Engineer will lead the development of AI solutions for the life insurance sector, overseeing full stack application design and delivery while guiding junior engineers.

Build core data and AI infrastructure to ingest, model, and serve unstructured healthcare policy data. Design backend systems, data pipelines, LLM-powered RAG/embedding pipelines, evaluation frameworks, and platform APIs. Improve data quality, auditability, and system reliability while iterating rapidly with founders.

Build and scale core AI-native healthcare products across the stack, focusing on real-time audio processing, NLU/transcription, clinical documentation, EHR integrations, and distributed production systems. Drive architecture, ship features with high ownership, and operate in a fast-moving, regulated environment.

Design and implement scalable graph-based search services and APIs. Build backend systems using Java and Spring Boot, integrate AWS services and databases, ensure observability with New Relic and Coralogix, and champion clean code and engineering best practices while collaborating across product and engineering teams.

The AI Success Engineer drives adoption of OpenAI products for government clients, ensuring technical readiness, mapping workflows, and guiding deployment while fostering strategic customer relationships and maximizing the value of AI solutions.

As an Applied AI Inference Engineer, you will architect, build, and deploy AI applications, collaborating with customers throughout the development and deployment process, and enhancing ML projects.

The AI Research Engineer will design and implement ML models, develop RL algorithms, and collaborate on integrating ML systems, enhancing AI experimentation.

Embed with cross-functional teams to map high-value workflows, design automated agentic or deterministic solutions, build production tools and integrations, monitor performance, run evaluations after model changes, and continuously improve AI-driven workflows while communicating impact to stakeholders.

The AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

The Senior Backend Engineer will build and operate backend systems for AI agents, focusing on reliability, API design, and data modeling, while enhancing code quality and performance in a fast-paced environment.

Design, build, and deploy AI/ML models and tools (NLP, RAG, predictive analytics) integrated with PHP/Laravel backends and MySQL. Develop inference and training pipelines, monitor and retrain models, integrate AI features into web platforms, and contribute to long-term AI strategy.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

As a Staff Software Engineer in Applied AI, you'll develop and deploy AI product features, collaborate with cross-functional teams, and advocate for customer needs, utilizing technologies such as TypeScript, Node.js, and LLM APIs.

Build and prototype AI-enabled internal tools and automations by partnering with cross-functional teams, validating high-leverage use cases, integrating LLM APIs and SaaS systems, and modeling secure, responsible AI practices to enable broader adoption.