AI/ML Engineer - Infrastructure & Systems

At Actualized Intelligence (ACTi), we are pioneering the next wave of AI-powered agents that facilitate real behavioral change. Our innovative technology and systems challenge the potential of conversational AI. We are searching for a skilled AI/ML Engineer to create and uphold the essential technical frameworks that support our expanding array of intelligent agents. You will play a crucial role in shaping the backend systems and data pipelines that enable our agents to perform effectively. We offer a hybrid work environment based in Paramus, NJ, alongside comprehensive benefits including health and dental insurances, retirement plans, and paid time off.

- Minimum of 3 years experience in software engineering, particularly focused on AI/ML infrastructure or backend systems
- Proficient in Python and experienced with AI/ML tools and libraries
- Practical experience with vector databases such as Pinecone, Weaviate, Qdrant, or similar
- Skilled in building and utilizing RESTful APIs along with LLM provider APIs (Anthropic, OpenAI)
- Familiar with workflow automation platforms like n8n, LangChain, or similar
- Experience in cloud computing environments (AWS, GCP, Azure) and container technologies (Docker)
- Solid knowledge of Retrieval-Augmented Generation architectures, embedding models, and knowledge retrieval systems
- Preferred: Experience with voice AI platforms (e.g., ElevenLabs, Deepgram)
- Preferred: Background in developing multi-agent systems or agent orchestration frameworks
- Preferred: History of fine-tuning LLMs or training custom models
- Preferred: Familiarity with CI/CD pipelines for AI/ML deployments
- Preferred: Experience in real-time data processing and streaming architectures

- Design and develop backend infrastructure for AI agent deployment, including API integrations, data pipelines, and orchestration workflows
- Oversee and enhance Retrieval-Augmented Generation (RAG) systems utilizing Pinecone vector databases, embedding pipelines, and knowledge base architecture
- Create and sustain n8n automation workflows that facilitate agent orchestration, data flow, and cross-platform integrations
- Integrate LLM APIs (Claude, GPT) with production systems, managing authentication, error handling, rate limits, and fallback logic
- Develop and maintain links with voice platforms (ElevenLabs), CRMs, communication tools, and other third-party services
- Establish the technical groundwork for the Builder Agent system, an autonomous agent-creation pipeline
- Implement fine-tuning workflows, manage embedding models, and frameworks for model evaluation when required
- Optimize infrastructure performance, latency, and cost efficiency across all AI systems
- Collaborate with the Prompt Architect to transform system prompt designs into durable, production-grade technical solutions