Senior Machine Learning Engineer (LLMs - Agentic Workflows)

We are seeking a skilled Senior Machine Learning Engineer to join our team, with a specialized focus on agentic workflows. The ideal candidate will have experience designing, developing, and deploying systems that transition LLMs from passive responders to autonomous agents capable of planning, tool-use, and self-correction.

Functional Responsibilities:

• Architect how the agent breaks down a complex user request into a series of actionable sub-tasks.
• Develop "Plan-and-Execute" or "ReAct" (Reason + Act) patterns where the model thinks before it acts.
• Design robust systems to maintain "short-term memory" across long-running tasks, ensuring the agent doesn't lose track of its goal or get stuck in infinite loops.
• Create the interface between the LLM and external software, databases, or APIs.
• Standardize how the agent calls functions, interacts with legacy systems, or executes Python code in a sandboxed environment.
• Implement error-handling and self-correction.
• Build custom evaluation frameworks to measure trajectory success—not just whether the final answer was right, but if the steps taken to get there were efficient and safe.
• Set up monitoring to visualize the agent's "thought process" and identify exactly where a multi-step workflow broke down.
• Ensure the agent doesn't "hallucinate" tool usage or take unintended actions through strict guardrails and Human-in-the-Loop (HITL) checkpoints for high-stakes decisions.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 5+ years of hands-on experience developing and deploying machine learning models in production environments.
• Strong software engineering fundamentals, including data structures, algorithms, system design, OOP, and API design & integration.
• Proven experience designing and implementing agentic architectures, including multi-agent workflows, tool-calling, state management, and human-in-the-loop patterns.
• Expertise in integrating Generative AI frameworks and APIs (such as LangChain, LangGraph, OpenAI, and Claude) into production-grade applications.
• Strong understanding of LLM fundamentals, systematic prompt engineering (chain-of-thought, few-shot), and debugging tools like LangSmith or Arize Phoenix.
• Experience with vector databases (Pinecone, Milvus, Qdrant) for retrieval-augmented generation (RAG) and long-term agent memory.
• Experience with cloud platforms such as AWS, GCP, or Azure for deploying AI workloads.