Architect MLE

Responsibilities

• Architect and develop end-to-end multi-agent systems from scratch
• Engineer advanced agent capabilities through custom tools and skills
• Implement context engineering for memory management and learning
• Deploy production-grade solutions on major cloud platforms, ensuring scalability
• Integrate and optimize LLMs as core reasoning engines for autonomous agents
• Maintain comprehensive tool libraries for agent interactions
• Establish metrics and KPIs for measuring agentic system performance

Qualifications

• 6-8 years of experience in machine learning and AI engineering with ML systems in production
• Expertise in building multi-agent systems and agentic workflows using Langraph/CrewAI
• Proficient in Python and ML frameworks including TensorFlow and PyTorch
• Hands-on experience with vector databases and scalable RAG systems
• Production-level cloud platform experience (AWS, GCP, or Azure), including compute and serverless services
• Strong skills in Infrastructure as Code and CI/CD pipelines
• Exceptional problem-solving, communication, and leadership abilities

Pay

$135,000 – $160,000 annually

Schedule

Remote - US based candidates only, no visa sponsorship available