AI ML Engineer

Key Responsibilities

• Design, build, and maintain backend infrastructure for AI agent deployment including API integrations, data pipelines, and orchestration workflows
• Manage and optimize Retrieval-Augmented Generation (RAG) systems using Pinecone vector databases, including embedding pipelines and knowledge base architecture
• Build and maintain n8n automation workflows that power agent orchestration, data flow, and cross-platform integrations
• Integrate LLM APIs (Claude, GPT) with production systems, handling authentication, error handling, rate limiting, and fallback logic
• Develop and maintain integrations with voice platforms (ElevenLabs), CRMs, communication tools, and third-party services
• Build the technical foundation for the Builder Agent system — an autonomous agent-creation pipeline
• Implement fine-tuning workflows, embedding model management, and model evaluation frameworks as needed
• Optimize system performance, latency, and cost efficiency across all AI infrastructure
• Collaborate with the Prompt Architect to translate system prompt designs into robust, production-ready technical implementations

Required Qualifications

• 3+ years of experience in software engineering with a focus on AI/ML infrastructure or backend systems
• Strong proficiency in Python and experience with AI/ML frameworks and libraries
• Hands-on experience with vector databases (Pinecone, Weaviate, Qdrant, or similar)
• Experience building and consuming RESTful APIs and working with LLM provider APIs (Anthropic, OpenAI)
• Familiarity with workflow automation tools (n8n, LangChain, or similar orchestration platforms)
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker)
• Strong understanding of RAG architectures, embedding models, and knowledge retrieval systems

Preferred Qualifications

• Experience with voice AI platforms (ElevenLabs, Deepgram, or similar)
• Background in building multi-agent systems or agent orchestration frameworks
• Experience with fine-tuning LLMs or training custom models
• Familiarity with CI/CD pipelines for AI/ML deployments
• Experience with real-time data processing and streaming architectures

What Success Looks Like

• Within 30 days: Fully onboarded on ACTi's current tech stack; able to independently deploy and troubleshoot agent infrastructure
• Within 60 days: Optimized existing RAG and n8n pipelines; reduced latency and improved reliability across live agents
• Within 90 days: Delivered first iteration of the Builder Agent infrastructure; established scalable patterns for rapid agent deployment