Senior Fullstack AI/ML Engineer

Join Boomi’s Enterprise AI Team — Where Innovation Meets Impact
Boomi’s Enterprise AI Team drives the creation of intelligent systems that augment business operations and customer experiences through AI-driven automation.As a Lead Forward Deployed Engineer, you’ll be the driving force behind the creation of cutting-edge internal AI platforms and agents—our Customer Zero prototypes that bring new AI capabilities to life before they reach the world.

In this role, you will:

Architect and deploy next-generation AI systems that blend automation, reasoning, and adaptability.Collaborate across R&D, platform engineering, and product teams to turn bold ideas into scalable realities.Experiment and innovate, pushing the boundaries of multi-agent collaboration, context-aware retrieval, and autonomous reasoning.

Serve as a trailblazer for AI-first development, influencing the roadmap of Boomi’s future products and intelligent infrastructure.

If you thrive at the intersection of engineering excellence, applied AI, and creative problem-solving, this is your chance to shape the future of intelligent business automation—starting from the inside out.
Be bold. Build smarter. Lead the AI revolution at Boomi.

Key Responsibilities

• AI Agent Design & Engineering:
  Architect, build, and orchestrate intelligent agents using ChatB, Agent Studio, and LLM frameworks (e.g., OpenAI, Anthropic, Claude, Gemini).
• Internal Platform Innovation (Customer Zero):
  Lead the design, experimentation, and validation of new AI workflows that drive measurable internal productivity improvements.
• Multi-Agent Systems:
  Design agent hierarchies and orchestration pipelines using coordination frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel).
• Applied Research:
  Investigate and integrate state-of-the-art AI techniques (tool use, reasoning, memory, reflection, RAG, and function calling).
• Integration Engineering:
  Build APIs and connectors enabling agents to interact with Boomi’s integration and automation ecosystem.
• System Architecture & Performance:
  Ensure that deployed agents meet enterprise standards for security, observability, and scalability.
• Knowledge Graphs & Retrieval:
  Implement vector databases, knowledge graphs, and retrieval pipelines to enhance context-aware responses.
• Evaluation & Metrics:
  Define KPIs, monitor LLM behaviors, and evaluate model performance through offline and live testing.
• Collaboration & Enablement:
  Work with platform, data, and UX teams to deliver integrated AI services and assist internal users with adoption and best practices.
• Documentation & Standardization:
  Develop reusable frameworks, templates, and design patterns for internal AI applications.
• Ethics & Governance:
  Support internal guidelines for AI transparency, data governance, and model safety within enterprise deployments.
• Tooling & MLOps Integration:
  Integrate pipelines with LLMOps, CI/CD, and API gateways for repeatable deployment.
• Mentorship & Evangelism:
  Mentor internal teams on AI agent architectures, experimental methods, and technical excellence.
   

Essential Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or Software Engineering (RQF Level 6+).
• Advanced coding skills in Python, TypeScript, or JavaScript.
• Experience with LLM-based development, RAG pipelines, and agent orchestration frameworks.
• Deep understanding of API design, microservices, and event-driven architectures.
• Familiarity with machine learning frameworks (PyTorch, TensorFlow, Hugging Face).
• Experience working with vector databases (Pinecone, Weaviate, Chroma, FAISS) and retrieval systems.
• Knowledge of data pipelines and ETL tools, especially integration with the Boomi platform.
• Exposure to AI ethics, model evaluation, and prompt optimization techniques.
• Experience deploying workloads to cloud platforms (AWS, Azure, GCP) using modern DevOps and infrastructure-as-code practices.
• Understanding of containerization (Docker, Kubernetes) for scalable AI deployments.
• Excellent communication and stakeholder engagement skills, capable of bridging research and engineering domains.
• Research-oriented mindset with a track record of innovation, rapid prototyping, and cross-functional collaboration.

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