Top AI Engineer Jobs

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Design, build, and operationalize reproducible AI/ML pipelines and automation for mission-critical systems. Troubleshoot CI/CD and containerized workloads, integrate AI capabilities with infrastructure and security, and support resilient hybrid compute operations to enhance warfighting decision support.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Design and own the decision-intelligence layer: build models and LLM systems to decide which claims to contest and offer amounts, create evaluation pipelines and golden datasets, architect deterministic vs learned components, close feedback loops from outcomes, and produce explainable recommendations for customers.

Own Soda’s technical content function across its website, blogs, webinars, videos, and audience growth. Build automated, agentic LLM content pipelines with structured outputs and evaluations while maintaining strong editorial judgment, distinct voices, and high-quality technical writing. Develop independently using Python or TypeScript, APIs, webhooks, and databases. Translate data quality and infrastructure concepts for technical audiences, collaborate with founders and customer engineers, and continuously improve workflows based on content performance.

The Senior AI Engineer will develop core AI systems for healthcare, focusing on real-time inference and NLP pipelines, ensuring safety and scalability in medical consultations.

The AI Engineer will manage AI systems, optimize prompts, evaluate new models, and build infrastructure while collaborating with engineering and product teams.

Designs, develops, and operates production data pipelines and curated, AI-ready data products for AbbVie R&D. Responsibilities include data integration, normalization, modeling, metadata, lineage, quality controls, governance, documentation, knowledge graph publication, and support for RAG and vector-search workflows. The role collaborates with scientists, engineers, architects, data owners, and vendors; provides technical guidance; monitors pipeline reliability; resolves issues; and ensures compliance with data security, GxP, and regulatory standards.

This role involves developing AI-powered analytics interfaces and tools, collaborating with a skilled team to enhance customer data experiences.

Lead design and build of production Generative AI solutions (document intelligence, RAG, semantic search). Implement and optimize LLMs, embeddings, and agentic workflows, deploy on Google Cloud/Vertex AI, apply MLOps, monitor performance, and collaborate with clients to translate requirements into scalable, cost‑efficient AI systems.

Design and develop AI systems for personalized travel and concierge services, focusing on integration and optimization of various technologies.

As an Applied AI Engineer, you'll build AI workflows for SEO, design LLM systems, and optimize operational quality, collaborating closely with a team.

Design, build, deploy, and maintain production AI solutions and agents using Python or .NET on Azure. Implement RAG pipelines, semantic search and vector DBs, work with large-scale data (Databricks/SQL), evaluate model performance, and collaborate with engineers and analysts to productionize experiments.

Lead the design, implementation, and enhancement of LLM-powered AI solutions, collaborating with teams to drive innovation and business outcomes.

The Senior AI Engineer will focus on building AI-driven pipelines, semantic technologies, and enterprise ontologies while mentoring and ensuring best practices in a hands-on role.

Support development and deployment of generative image models on Azure, including training/fine-tuning, prompt engineering, image preprocessing/post-processing, integration into tools, testing, documentation, and establishing repeatable workflows for industrial imagery.

The Senior AI Engineer will implement AI solutions, consult stakeholders, lead projects, and ensure integration of cutting-edge AI technologies to meet business goals.

As a Senior AI Engineer, you'll implement AI solutions, facilitate customer workshops, lead development projects, and align strategies with business objectives, utilizing Azure and OpenAI technologies.

Lead the technical direction of AI agents, managing context and workflows while partnering with teams to align AI initiatives with business goals. Oversee architectural decisions and mentor engineers in AI practices.

The AI Engineer will design and develop innovative frameworks for trading and research, conducting large-scale experiments and collaborating with a team on foundational models and cloud platforms.

Develop and support AI/ML applications and infrastructure for a federal healthcare agency. Build CI pipelines, automate deployment and infrastructure processes, manage cloud and server environments, troubleshoot applications and networks, monitor performance, and implement security and data protection. Collaborate with architects, developers, DBAs, stakeholders, and Agile teams while conducting code reviews, documenting solutions, mentoring colleagues, and supporting AI/ML prototyping, evaluation, integration, and production deployment.

Gather client requirements and architect, develop, and deploy cloud data platforms, warehouses, ETL processes, pipelines, APIs, and BI solutions. Perform advanced SQL and data modeling, support deployments and integrations, diagnose technical issues, mentor staff, contribute to innovation and practice development, and assist with project management. The role requires approximately 20% overnight travel and primarily in-person work.

Lead platform engineering strategy across multi-environment and multi-region cloud systems. Architect Kubernetes and container platforms, establish infrastructure-as-code standards, and implement DevSecOps, security, compliance, reliability, observability, and cost optimization practices. Lead cloud migrations and AI/ML infrastructure initiatives involving model serving, GPU orchestration, LLM gateways, vector stores, and CI/CD. Mentor engineers, guide architecture decisions, communicate with stakeholders, and serve as a technical escalation point.

Leads AI/ML-focused DevOps, application development, and infrastructure initiatives. Builds CI pipelines, automates deployment and operational processes, manages cloud and server environments, troubleshoots systems, monitors performance, and implements security measures. Collaborates with architects, developers, DBAs, stakeholders, and users to deliver reliable applications and AI/ML capabilities. Participates in Agile ceremonies, code reviews, documentation, incident management, mentoring, and technical planning.

Leads AI and software application development for AHRQ, coordinating developers and DBAs, implementing AI strategy, and supporting Agile delivery. Designs reusable, scalable code; integrates and deploys AI/ML capabilities; develops and evaluates generative AI and LLM solutions; maintains CI pipelines; optimizes performance; implements security and data protections; conducts code reviews; documents technical processes; and mentors development staff.