Top AI Engineer Jobs

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Lead Engineer to architect, build, and deploy production-grade GenAI and agentic AI systems grounded in knowledge graphs. Responsibilities include neuro-symbolic design, knowledge-graph modeling and databases, agent orchestration, LLM integration, scalable APIs, data pipelines, governance, reliability, and mentoring engineers across enterprise domains.

Embed with business units to prototype, deploy, and operationalize AI applications. Architect and build backend services, APIs, containers, and streaming data pipelines (Kafka/Kinesis). Deploy on cloud-native platforms (Docker, Kubernetes/EKS/ECS), integrate LLMs/agentic systems, and lead cross-functional delivery from scoping through production and operational excellence while providing field feedback to platform teams.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Support Regulatory Affairs by evaluating AI and emerging technologies, assisting with pilots and POCs, documenting requirements and process maps, contributing to process improvement, and ensuring responsible, compliant AI use while maintaining project documentation and collaborating across technical and business teams.

Build and ship full-stack, AI-powered features using agentic systems. Contribute to code reviews (including AI-generated code), collaborate with product and senior engineers, maintain multiple projects using agents, and iterate quickly in a hybrid Boston office environment.

Develop and maintain human-centered AI software across the SDLC. Collaborate with PIs and cross-functional teams, track requirements in GitLab, produce API/docs, and implement deployable solutions using Python, databases, containerization, and cloud technologies in an Agile environment.

The AI Platform Engineer builds and operates an enterprise AI platform, focusing on core services for LLMs and RAG systems, automates onboarding processes, and ensures compliance and operational excellence within the platform team.

Design, develop, test, document, and deploy state-of-the-art AI/ML applications (LLMs, deep learning, CV, sensor/data fusion). Collaborate with cross-functional teams to integrate solutions into development and production, create software and test requirements, and perform rapid prototyping and proofs-of-concept while following software engineering best practices.

Lead technical direction and hands-on development of ML and AI Context systems for HubSpot CRM: model development, productionization, retrieval, embeddings, evaluation, monitoring, and cross-team architectural leadership to deliver measurable product and business impact.

Build and evolve Ridgeline's AI data platform: design secure API integrations and MCPs, implement RAG pipelines and vector database solutions, scale distributed systems, enforce data lineage/access controls, and mentor peers while shaping AI platform strategy.

Lead and execute Generative AI strategy for Global Markets Regulatory Reporting. Define engineering approach, design scalable GenAI solutions, set technology stack, oversee cross-team design and testing, integrate GenAI tools, and drive implementation for multi-tier, microservices-based platforms to deliver business outcomes.

Design, develop, test, and maintain AI-enabled enterprise software and integrations. Build AI-powered features using LLMs, prompts, and retrieval approaches; develop APIs, cloud components, CI/CD pipelines, and tests; troubleshoot, optimize, and support production systems while following secure development and Agile practices.

Build and ship internal AI enablement tools, workflows, and guardrails to help engineers use coding agents and LLMs safely and productively. Evaluate models and agents, partner with engineering teams, measure adoption and impact, and provide documentation, training, and hands-on support to increase engineering velocity and quality.

Designs, ships, evaluates, secures, and supports AI-native production systems and agentic AI tooling. Uses AI coding assistants across the SDLC, leads design and requirements, performs technical and code reviews, manages on-call and monitoring, owns change requests and enhancements, and coordinates vendor evaluations and cross-team integration.

Designs, builds, evaluates, secures, and supports production AI-native software and agentic systems. Uses AI coding assistants across the SDLC; performs model evaluation, testing, integration, production support, and stakeholder-driven requirements analysis while enforcing secure, privacy-aware practices.

Design, optimize, and deploy ML and LLM capabilities for resource-constrained heterogeneous edge platforms. Build efficient inference pipelines, RAG/semantic search, distributed inference orchestration, model optimization (quantization/pruning/distillation), monitoring, evaluation, and AI governance. Integrate models into production software, support multimodal and sensor analytics experiments, document results, and assist with customer demonstrations and prototypes.

Owns the company-wide IT and cybersecurity program, including SOC 2 compliance, cloud security, penetration-test remediation, business continuity testing, endpoint management, access controls, onboarding and offboarding, AI security governance, vendor reviews, security training, incident response, and employee IT support. The role partners with engineering, HR, Finance, leadership, auditors, and healthcare customers while managing security across AWS, Heroku, Cloudflare, Google Workspace, GitHub, and related tools.

Own and operate Baseten's internal AI stack, build and deploy custom AI agents and LLM-powered tools, evaluate third-party AI developer tools, instrument usage and measure impact on engineering productivity, and support engineering teams in adopting AI workflows.

Develop and deploy end-to-end Generative AI proof-of-concepts on Azure and AWS Bedrock. Build full-stack web applications, integrate AI models/APIs, manage CI/CD and containerized deployments, prototype rapidly, collaborate with cross-functional teams, and ensure security, compliance, and ethical AI practices.

Partner with business stakeholders and IT delivery teams to design, implement, and deploy AI-enabled production applications, integrations, and data pipelines. Focus on solution feasibility, deployment, operationalization, and driving adoption. Support benchmarking and internal development across divisions (travel up to 50%).

Design, build, and operate cloud-scale, multi-tenant services for Microsoft Edge and Copilot App management. Lead architecture, cross-team integrations, reliability, security, and production operations. Mentor engineers, drive technical direction, and use telemetry and customer feedback to improve enterprise management and AI-powered productivity experiences.

Develop, test, deploy, and maintain .NET-centric web applications using C#, Angular 17+, and Azure. Build and consume REST APIs, work with SQL Server, source control, CI/CD pipelines, and collaborate on code reviews, debugging, performance optimization, and third-party integrations.