Jobs at Artefact (artefact.com)

Lead AI and machine learning engineering teams delivering production-grade classical ML, LLM, and agentic systems. Own solution architecture, context and harness engineering, fine-tuning pipelines, cloud platform strategy, and technical standards. Partner with clients and senior leadership while supporting pre-sales through proposals, demos, proofs of concept, and solution scoping. Manage and mentor engineers, oversee model development, and guide scalable, reliable, cost-efficient AI deployments.

Own end-to-end data science and AI solutions, from business problem framing through production deployment and monitoring. Build statistical and machine learning models for forecasting, classification, clustering, and causal analysis; conduct LLM fine-tuning and evaluation experiments; develop data pipelines, dashboards, and evaluation suites; and advise clients on AI architecture decisions. Present findings to nontechnical stakeholders and mentor junior data scientists.

Manages AI and data consulting projects, including planning, delivery, quality, client relationships, and team development. Translates client needs into strategic recommendations, prototypes solutions with AI coding tools, and applies concepts such as RAG, semantic layers, MCP, and context engineering. Coaches consultants in AI-native workflows, supports executive communications, contributes to proposals and business development, and helps expand client engagements.

Build and deploy full-stack AI applications, including React interfaces, Python or Node.js services, agentic workflows, RAG pipelines, data integrations, evaluations, and cloud infrastructure. Ensure production reliability through testing, monitoring, CI/CD, observability, and infrastructure as code. Work directly with clients, support pre-sales, mentor junior engineers, and contribute to reusable engineering accelerators.

Lead marketing measurement and effectiveness programs using marketing mix modeling, incrementality testing, attribution, causal inference, experimentation, and optimization. Own statistical methodology, production deployment, GenAI applications, client relationships, executive communication, team development, and account growth. Translate models into budget and media decisions, build reusable measurement accelerators, uphold scientific rigor, and remain hands-on with technical problem-solving, MLOps, cloud industrialization, and model review.

Build end-to-end, production-grade AI applications using Gemini Enterprise and the Google AI stack. Implement agentic systems, RAG pipelines, retrieval and data integrations, front-end interfaces, backend services, deployments, and monitoring. Work embedded with clients, translate requirements into technical solutions, write evaluations and tests, and mentor junior engineers while contributing to reusable accelerators and pre-sales efforts.

Lead and grow an AI and ML engineering team to design, build, and deploy production-grade AI solutions (classical ML, LLMs, agentic systems). Define context engineering, agent harnesses, fine-tuning pipelines, and platform architecture (primarily Gemini/Vertex AI). Drive pre-sales, client engagement, technical strategy, and best practices for observability, guardrails, and MLOps.