Top AI Engineer Jobs

Design, build, and operate AI-powered full-stack applications and agentic workflows on GCP. Own architecture, IaC, CI/CD, integrations with enterprise SaaS, databases, and production-grade RAG/LLM pipelines. Automate developer workflows and ship scalable, secure systems.

Lead architecture, development, and production deployment of AI/ML solutions for cybersecurity, network analytics, and enterprise risk detection. Build and operationalize models (anomaly detection, predictive, classification), implement Generative/Agentic AI, design scalable ML pipelines, collaborate with security and IAM teams, and deliver dashboards and visualizations for stakeholders.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

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.

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.

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.

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.

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.

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.

Develop and validate system-level software for neuromorphic accelerator platforms, including device-driver interfaces, OS integration, runtime APIs, simulation environments, and tooling for profiling, debugging, benchmarking, and validation. Collaborate with hardware, architecture, and application teams to enable platform features, improve performance, and ensure robust functionality for production deployments.

Design, build, and operate a secure, high-performance agent sandboxing platform. Ensure isolation, reproducibility, and efficient resource use across containerized/virtualized environments. Improve developer experience (APIs, client SDKs, docs), debug and monitor production incidents, lead architecture and end-to-end projects, and collaborate with internal teams to shape roadmap and tooling.

Lead enterprise AI transformation by discovering opportunities, designing and building scalable LLM/agent-based workflows, architecting data and integration solutions, partnering with cross-functional stakeholders, and driving production delivery and adoption across business units.

Ship and operate an LLM-backed dissolution support product: design retrieval, tool contracts, structured outputs, observability, and safe human handoffs; build versioned evaluations, privacy/tenant safeguards, rollout/canary plans, and iterate on production failures to improve product reliability and customer outcomes.