Applied AI Engineer

• Partner with product owners and finance experts to identify strong AI use cases in Accounts Receivable.
• Design and build predictive models, generative AI features, and multi-agent systems tailored to business problems.
• Develop LLM-based copilots and autonomous agents, including human-in-loop flows, security guardrails, and audit trails.
• Work closely with software engineers and data scientists to embed AI features into the product.
• Build and maintain secure, scalable MLOps pipelines for training, deployment, and monitoring.
• Continuously evaluate and improve model performance and user impact.
• Stay current with the latest advancements in LLMs, NLP, multi-agent frameworks, and applied AI engineering.
• Set up and tailor AI demos as needed for customers, stakeholders, and internal teams.
• Contribute to a culture of innovation, collaboration, and continuous learning.

Required Experience

AI/ML/LLM Experience

• 4+ years of AI/ML engineering experience.
• 3+ years of hands-on experience with LLM-based solutions.
• 1+ year of experience building agentic systems (production-ready, not POCs).

Multi-Agent & Autonomous Systems

• Strong understanding of multi-agent architectures, memory systems, tool-use, orchestration, routing, and safety guardrails.
• Experience building autonomous or semi-autonomous agents with governance, logging, and human-in-loop flows.

Technical Skills

• Strong programming skills in Python (preferred), R, or Java.
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Solid understanding of predictive modeling, NLP, ML algorithms, and statistics.
• Experience working in cloud environments (AWS/Azure/GCP) with model deployment and data pipelines.
• Knowledge of GitHub/GitLab, version control, and CI/CD practices.
  • Basic understanding of web fundamentals (HTTP, JSON, authentication) and REST APIs to integrate AI/LLM solutions into the product.