Top AI Engineer Jobs

Embedded with strategic customers, own end-to-end design, build, and production rollout of cloud infrastructure for large-scale GPU/HPC AI workloads. Build compute orchestration, platform services, onboarding sandboxes, and reliability/security tooling. Turn field learnings into reusable platform capabilities and partner with Product and Engineering to productize them.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

Lead design, implementation, and deployment of scalable AI/ML systems for aerospace and defense. Develop RAG, embedding/retrieval pipelines, transformers, GANs, RL and MPC-aligned models. Use GPU/HPC for real-time decisioning, mentor engineers, ensure safety, validation, regulatory and cybersecurity compliance, and support integration and prototyping.

Conduct AI research to develop new approaches, algorithms, and methodologies for portfolio companies; influence technical direction, drive AI system improvements, and collaborate with founders to accelerate growth and impact.

Design, evaluate, and deploy production AI agents that combine LLMs, retrieval, memory, and traditional ML. Build retrieval and memory pipelines, multi-agent systems, evaluation frameworks, and production guardrails. Run experiments and ablation studies, collaborate with infrastructure and enterprise customers, and measure business impact while ensuring reliability, safety, and observability.

Design, evaluate, and deploy production AI agents combining LLMs, retrieval, memory, and tool use. Build retrieval/memory pipelines, multi-agent systems, evaluation frameworks, and production engineering for reliable, safe enterprise AI deployments while partnering directly with customers.

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.

Design, build, and operate the C3 Agentic AI Platform for production AI agents. Architect scalable, reliable distributed systems; improve agent accuracy, performance, and safety; implement guardrails, auditability, and rollback; collaborate with data scientists and product owners; own end-to-end projects and mentor junior engineers.

Lead design and engineering of enterprise AI-powered automation using n8n as an orchestration platform. Build scalable, secure API-first and event-driven integrations, AI-enabled workflows and agents, CI/CD and governance, observability, and reusable automation frameworks. Provide technical leadership, mentor engineers, and drive platform standards and operational excellence.

Senior product-minded engineer owning end-to-end SaaS product areas: discovery, design, implementation, testing, and iteration. Translate ambiguous goals into workflows and technical plans, make architecture decisions, build polished UX, use and validate AI-assisted development tools, communicate clearly, and drive work independently with cross-functional partners.

Lead design, development, and testing of backend services and APIs (primarily Rust) for quantum security products. Own architecture, reliability, performance, and quality; use AI-assisted tooling; mentor engineers; collaborate across hardware, product, and engineering.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Design, build, and deploy AI/ML and optimization solutions for Department of War customers. Implement ML pipelines on AWS GovCloud and Databricks, maintain PySpark/Delta Lake pipelines, develop feature stores, ensure model governance (bias, explainability, monitoring), produce performance reports, translate mission requirements into model specs, and mentor junior engineers.

Build and maintain backend services and APIs (primarily Rust) to deliver quantum entropy and keys securely. Use AI-assisted tooling, participate in design/code reviews, collaborate with hardware and product teams, and develop domain knowledge in quantum RNG, cryptography, and network security.

Lead the development and operation of a repeatable risk review practice across cloud, application, vendor, identity, and emerging AI systems. Perform threat modeling and risk reviews, define secure-by-default standards and controls, embed risk into procurement and delivery, mentor engineers, and drive cross-organizational governance to improve risk posture and program maturity.

Design, develop, and maintain full-stack enterprise SPAs and backend services; build and optimize enterprise search and RAG/LLM integrations; create data ingestion pipelines, embeddings, and vector search; participate in Agile ceremonies, CI/CD/DevSecOps, testing, documentation, and stakeholder collaboration to deliver scalable AI-powered applications.

Design Verification Engineer responsible for verifying Tensix IP, building reusable SystemVerilog/Python testbenches and DV infrastructure, developing constrained-random and directed tests, closing coverage, debugging RTL/testbench/workload issues, and integrating CI/CD, simulation/emulation, and containerized regression systems. Collaborates with design, firmware, compiler, and architecture teams.

Design and build agentic components and workflows integrating LLM APIs, embeddings, vector stores, and agentic frameworks. Work on context engineering, RAG, evaluations and guardrails, telemetry and observability, on-call support, and cross-functional collaboration to deliver production AI agent features on cloud platforms.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

Design, develop, deploy, and maintain AI/ML models and MLOps pipelines; optimize model performance; run experiments; integrate models into production systems; collaborate with cross-functional teams to translate ML research into mission value.

Lead and manage an ML Models-as-a-Service team to define vision, build and deploy production ML models and APIs, establish MLOps and CI/CD pipelines, enforce governance and security, collaborate with stakeholders, and oversee project delivery and resource allocation across the model lifecycle.

Design and implement agentic AI integrations: build retrieval and ingestion pipelines, embeddings and vector-store indexing, develop APIs/microservices, optimize query routing and caching, implement auth/audit, and support prototypes, demos, and pilot deployments.

Design and build end-to-end AI-native solutions including backend agent orchestration and production-ready frontends. Develop GenAI systems using LLMs, autonomous agents, vector search, and RAG. Rapidly prototype MVPs, deploy cloud-native architectures, and create accessible web interfaces and dashboards for non-technical users.