Top AI Engineer Jobs

Senior technical lead for APAC partners driving partner enablement, co-selling, and technical leadership around Talkdesk CCaaS/CXA. Responsibilities include advanced demonstrations, solution design, ecosystem integrations, Agentic AI implementations, partner competency development, and regional representation with ~35-70% travel.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

Build and ship applied AI features across web and mobile: integrate agents/LLMs into React/React Native apps and Node.js/Postgres APIs, design real-time matching and vetting systems, partner with design, and own product-engineering work on an early-stage team.

Design, build, and ship production AI agents and their infrastructure on frontier LLMs. Embed with customers to create agent harnesses, orchestration, integrations (WMS/TMS/ERP), and evaluation pipelines. Own data, services, APIs, and repeatable deployment patterns to scale agent deployments.

Lead design and implementation of Traba's agent platform: orchestration runtime, eval and observability stacks, integrations to customer systems (WMS/TMS/ERP), model strategy, retrieval/memory, and production agent deployments. Spend time with customers, build evaluation pipelines and regression suites, hire and mentor engineers, and set multi-year roadmap with CTO and product teams.

Build and own production AI agents and their infrastructure: harnesses, evals, orchestration, and model strategy. Embed with customers, design agent integrations with WMS/TMS/ERP, ship reliable agentic features on frontier LLMs, and codify repeatable deployment patterns.

Embed with business teams to identify workflows, rapidly prototype, and deliver production AI applications using LLMs, agents, RAG, and enterprise integrations. Build full-stack solutions, integrate with platforms (Microsoft 365, Salesforce, Oracle), deploy to cloud, and create reusable AI components and standards to drive enterprise adoption and measurable business value.

Lead design and implementation of AI compiler architecture for automotive SoCs, including graph optimizations, lowering, code generation, NPU performance tuning, hardware-software co-design, KPI ownership on real workloads, and mentoring technical staff.

Design, build, deploy, and maintain AI/ML models for immigration-related datasets in classified AWS environments. Analyze complex data, operationalize models via DevSecOps CI/CD and containerization, present results, and provide security architecture and identity-management technical leadership for a cleared federal customer.

The HPC & AI Performance Engineer will execute benchmarking projects, analyze performance, and optimize HPC systems using scientific software and methods.

Design and build production-ready AI applications leveraging LLMs. Develop backend services orchestrating LLM interactions, prototype user interfaces, implement RAG and vector search, integrate enterprise data sources, monitor AI performance, apply prompt engineering, ensure responsible AI practices, and collaborate with cross-functional teams to deliver scalable AI-enabled solutions.

Design, train, test, deploy, and maintain end-to-end AI/ML systems for production. Select algorithms, architect MLOps pipelines, optimize models for high-throughput text/image/video feeds, and collaborate with engineers and product owners to deliver mission-critical solutions for federal and defense clients.

Develop electro-optical architectures and optimize integrated electro-photonic components for next-generation optical interconnects. Lead cross-functional R&D, create simulation methodologies and mixed-signal/digital accelerators, advise post-silicon test plans, and document designs for scalable electro-photonic systems.

Lead operations for large-scale AI infrastructure: manage NVIDIA GPU and high-performance networking environments, troubleshoot Linux/Kubernetes/storage/hardware issues, drive incident response and root cause analysis, improve observability and automation, mentor engineers, and collaborate with engineering and datacenter teams to ensure platform reliability and scalability.

Lead architect and hands-on builder for AI solutions: evaluate feasibility and data readiness, translate business problems into technical and algorithmic requirements, design high-level architectures and prototypes, drive PoC to production handover, set MLOps/API standards, mentor team, and align North American implementations with European enterprise governance.

Build and maintain Python data pipelines and integrations to make enterprise data discoverable. Develop metadata enrichment, semantic knowledge layers, vector embeddings, and search/index tuning. Maintain Databricks/AWS Glue pipelines, sync taxonomies/ontologies, build APIs/MCP servers, and troubleshoot data quality and pipeline issues to support retrieval-augmented generation and AI-powered discovery.

Lead design and development of enterprise software solutions using Java and Cursor AI. Provide technical leadership, mentor engineers, define architecture and roadmaps, implement cloud-native, microservices, CI/CD and infrastructure-as-code practices, and collaborate across product and engineering teams to deliver performant, secure systems.

Own operational readiness and productionization for internal AI-enabled tools: build and maintain CI/CD and IaC, support Vercel and multicloud deployments, improve monitoring, secrets and access controls, runbooks, and lifecycle standards. Partner with Corporate AI, IT, Security, and business teams to move prototypes to production and ensure reliability, security, and maintainability.

Design, build, and operate a LangGraph-based multi-agent orchestration platform for production AI: agent integration contracts, stateful multi-turn context management, guardrails for PHI/clinical safety, RAG retrieval pipelines (Azure AI Search), versioned prompt architecture, evaluation/regression suites, and integrations to commercial and internal agents. Collaborate with cross-functional teams to implement, test, and maintain agentic systems in a regulated domain.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Build and ship production LLM workflows for contract review, redlining, extraction, and legal Q&A. Design agent orchestration and multi-step reasoning pipelines, diagnose and fix model failures, work with customers to turn legal problems into reliable AI systems, and own features end-to-end from design to production.

The Senior AI Engineer will architect LLM agents, drive ML lifecycle processes, build GenAI solutions, and lead a data engineering team. Responsibilities include overseeing data architecture, scalable deployments, and mentoring.

The Lead AI Engineer will build LLM-powered features, optimize AI infrastructure, and lead the execution of AI projects. Responsibilities include designing pipelines, evaluating models, and collaborating with product teams.

The Founding AI Engineer will develop an end-to-end scan-to-BIM pipeline, focusing on 3D scene understanding and BIM generation from spatial data.

As a founding ML research engineer, you'll design AI-powered engineering systems, implement ML algorithms, and integrate deep learning techniques while collaborating with experts in the field.