Part-time contract position (≈10–20 hrs/week) to assess AI coding agents' interactions end-to-end. Responsibilities include judging usefulness and alignment with strong engineering thinking, assessing explanations and reasoning, distinguishing response quality levels, and providing clear, opinionated feedback. Targeted at staff/principal-level engineers with hands-on experience in Python, TypeScript/JavaScript, and familiarity with Codex, Claude Code, or Cursor.

Join SecurityScorecard as a Senior Data Scientist to develop and productionize machine learning models for risk scoring, threat intelligence, and vendor risk assessment. Responsibilities include exploratory data analysis, model evaluation and validation, collaborating with product and engineering teams, and integrating models into production. Required: advanced quantitative degree or equivalent, 5+ years applying data science/ML, proficiency in Python and frameworks (scikit-learn, XGBoost, PyTorch, MLflow), and experience with cloud data pipelines (AWS/GCP). Prior cybersecurity or LLM exposure is a plus. No immigration sponsorship provided.

The Director, AI Governance & Enterprise Solutions is the senior AI technical lead responsible for the Momentive Context Library, defining and enforcing technical standards for AI agents, leading agent review and approval, building agents and tooling for the AI Operations Hub, and advising spoke teams on architecture, deployment, monitoring, and security. Requires 7+ years in software/AI systems with at least 3 years working with LLMs, agent architectures, and production AI deployments.

Lead design and optimization of enterprise MarTech architecture, build AI agents for campaign workflows, drive MarTech integrations and migrations, and establish architecture standards and governance. Requires 10+ years in marketing technology/platform architecture and 5+ years designing AI/ML or intelligent automation solutions; deep experience with MarTech platforms (CRM, marketing automation, CDP, CMS), LLM integration, API/data/cloud architecture, and tools like Adobe Workfront and Marketo.

Hiring a Senior Python + GenAI Engineer to develop Generative AI solutions using LLMs and RAG pipelines, build multi-turn conversational and multi-agent systems, integrate APIs with enterprise systems (HR/payroll), and implement observability and model evaluation. Requires 5+ years of software development experience, strong Python skills, hands-on experience with LLMs/RAG/vector databases, API development, and working knowledge of AWS. Remote role targeting Latin America; strong English communication required.

Senior AI Full Stack Engineer (remote, LATAM) responsible for developing high-performance big-data applications and cloud-native Azure data pipelines (including ADF), building full-stack applications with React/Angular/Vue and backend services (Node.js/Python/Java), and implementing APIs and microservices. Requires 5+ years experience, strong proficiency in Scala/Databricks/Spark/Python, experience with AI-augmented development (e.g., Vibe Coding), CI/CD and distributed system design; experience with marketing ecosystems (ESP, CDP) is preferred.

Coforge busca Ingeniero de IA para Lima (modalidad híbrida, asistencia presencial 2 veces por semana). El rol implica diseñar e implementar agentes y asistentes inteligentes, arquitecturas de IA, pipelines RAG, integración con bases de datos vectoriales, desarrollo de APIs y despliegue/monitoreo en producción. Requiere formación universitaria relacionada, especialización en IA y experiencia práctica en soluciones de IA generativa, buenas prácticas de ingeniería de software y herramientas como LangChain, LLMOps y tecnologías cloud.

The Sr. Machine Learning Engineer will collaborate with product teams to design, develop, deploy, and monitor production AI/ML solutions, including LLM-powered applications, RAG systems, knowledge graphs, scalable data pipelines, and MLOps practices. Preferred experience includes Python, PyTorch/TensorFlow, GCP (Vertex AI, BigQuery), vector databases, model serving, CI/CD, and production monitoring. Role typically reports to a Software Engineering Manager and may require 5–20% overnight travel. A pay range of $100,000–$180,000/year is listed for certain U.S. residents in the posting.

Lead end-to-end product and engineering efforts to improve Copilot code review quality, context, memory, and evaluation infrastructure. Responsibilities include designing evals and metrics, making quality/latency/cost tradeoffs, shaping experiences across VS Code/Copilot app/CLI, and keeping distributed services healthy (including on-call).

Senior technical leadership role responsible for architecting multi-tier .NET solutions, designing cloud (Azure/AWS) high-availability architectures, integrating AI/LLM capabilities (including prompt engineering and AI-assisted coding), leading multiple engineering teams on solution design, and implementing DevOps, CI/CD, and IaC practices. Requires 11+ years of experience and strong hands-on skills across .NET stack, frontend (React), databases, containers (Docker/Kubernetes), Terraform/ARM, and data platform design.

Senior applied research scientist role at NVIDIA to invent and evaluate AI-assisted numerical methods (e.g., learned preconditioners, AI-guided multigrid, differentiable solver components) for large-scale scientific and industrial simulation. Requires PhD or equivalent, 5 years' relevant experience, and experience with ML frameworks (PyTorch/JAX), Python, GPU computing, and numerical methods (PDEs, sparse linear algebra, iterative solvers).

NVIDIA is hiring a Senior Software Engineer for Agent Simulation & Evaluation to develop agentic automation, tools, and performance models for evaluating LLMs, reasoning and coding agents, and heterogeneous GPU–LPU inference workloads. Requires strong Python, experience with AI agents and inference workloads, simulation/profile/benchmarking expertise, and 5+ years of relevant software development experience.

Lead and architect scalable, secure GenAI solutions combining LLMs, RAG, vector DBs and agent workflows. Own backend platforms for inference and RAG pipelines, define frontend AI-native UX (React), drive CI/CD/QA, integrate cloud/on-prem AI, optimize models and ML pipelines, ensure security/privacy/governance, and guide multiple pods/teams. Requires extensive hands-on experience in Python, modern frontend frameworks, cloud platforms (AWS/Azure/GCP), distributed systems, and demonstrated delivery of production Generative AI at scale.

Lead the architecture and technical vision for AI-powered, user-facing applications built with Python, React, and Generative AI. Responsibilities include designing scalable LLM inference and RAG platforms, defining frontend AI-native UX patterns (conversational interfaces, copilots), establishing standards for prompt design and model integration, driving reuse via SDKs/frameworks, ensuring enterprise readiness (security, privacy, governance), and influencing technical strategy across teams. Requires 11+ years total experience with strong experience in software engineering, cloud platforms (AWS/Azure/GCP), distributed systems, and stakeholder influence.

Lead architecture and technical vision for AI-powered user-facing applications built with Python and React. Design scalable, secure backend platforms for LLM inference, RAG pipelines, and agent orchestration; define frontend/UX patterns for conversational interfaces and copilots; establish standards for prompt design, model integration, evaluation, and observability. Requires 9+ years of experience, cloud (AWS/Azure/GCP) familiarity, distributed systems knowledge, and strong communication to influence senior stakeholders.

Lead and grow engineering teams building AI/GenAI (including LLMs and Agentic AI) solutions to automate compliance operations. Responsible for designing, delivering, and operating multi-tenant distributed cloud systems at scale, driving engineering quality, architecture reviews, incident response/on-call participation, and cross-functional collaboration with Product and Compliance. Requires at least 10 years software engineering experience and 5 years engineering management (including 2 years managing managers).

Technical leader responsible for architecting and driving implementation of large-scale, low-latency distributed inference systems and full model lifecycle management. Requires deep expertise in GPU architecture and low-level performance tuning (CUDA, kernels), cloud-native multi-tenant model serving, orchestration (Kubernetes, Ray), and open source ecosystem engagement. Minimum 16+ years technical experience and significant leadership experience (7–10+ years cited).

NVIDIA's Silicon Co-Design Group is hiring an Applied AI Engineer to architect and implement AI-driven solutions (LLM-powered validation pipelines, orchestration agents, and data systems) for post-silicon validation and chip/productization workflows. Requires 5+ years building/deploying ML/AI or data-intensive backend systems, 2+ years owning applied AI/LLM workflows end-to-end, strong Python and at least one static language (C/C++/C#/Java/Scala), and experience with production test, system validation, and silicon development environments.

Capital One is hiring a Distinguished AI Engineer to design, develop, deploy, and support foundation model training, LLM inference, similarity search, guardrails, model evaluation, and observability at scale. The role requires deep engineering and ML expertise (Python, Go/Scala/Java/C++/C#), experience with cloud platforms (AWS/GCP/Azure), and demonstrated ability to optimize training and inference for scalability, latency, throughput and cost. The position is remote-eligible and Capital One may sponsor employment authorization.

Red Hat seeks an Architect, AI Platform to lead design and delivery of container-native MLOps pipelines and production-ready AI solutions using Red Hat OpenShift and OpenShift AI. The role involves architecting model deployment (KServe, vLLM, TGI, Triton), automated benchmarking, customer-facing consulting and PoC work, mentoring, and producing architectural documentation. Remote (US) role requiring substantial experience in cloud, Linux, Kubernetes/OpenShift, and production AI systems.