Agentic AI Engineer (Xora Portfolio Company)

ELEMYNT is an early-stage startup building applied intelligence for materials design. This role focuses on building the LLM-powered agent layer: provider abstraction, agent orchestration, retrieval, evaluation, and observability.

• Build the provider abstraction that lets any workflow call, swap, or add a model provider by configuration, across commercial APIs and self-hosted endpoints, with structured-output validation, retries, and cost tracking.
• Build the agent orchestration where a planning agent dispatches specialized sub-agents in parallel on a stateful framework, with durable checkpoints, conditional branching, and the context and memory management that keeps multi-step workflows coherent across long task horizons.
• Build human-in-the-loop checkpoints so low-confidence or high-stakes steps route to a person before an agent proceeds.
• Wrap existing platform capabilities as typed, registered tools the agents call, with a clean boundary between the agent layer and the systems it builds on.
• Design retrieval end to end, from ingestion, embeddings, and chunking through hybrid search and reranking, and assemble the context that grounds each model call.
• Build the prompt layer: versioned prompts, few-shot sets, and captured reasoning, so every change is tracked and every call is inspectable.
• Expose agents and guardrailed model access as tools behind one integration point that backend services, the frontend, and notebooks all consume.
• Instrument every model call, tool invocation, and agent run as traced spans with prompt, model, and tool lineage, so behavior and cost stay debuggable.
• Build the evaluation framework, deterministic trace metrics alongside LLM-as-judge scoring for faithfulness, that gates changes and catches regressions before they ship.

• Bachelor's or Master's degree in Computer Science or a related engineering field, and 5+ years building and shipping production software, with real depth building LLM or agent systems in production.
• Strong Python and solid engineering practice: async code, typing, testing, modular design, and code review, plus a track record of shipping systems others depend on.
• Hands-on experience building agentic or LLM systems in production: orchestration loops, tool-calling, structured outputs, and context and memory management for reliable long-running workflows.
• Experience working across multiple model providers behind a single abstraction, with routing, fallback, and a feel for the cost and latency trade-offs.
• Experience building retrieval systems end to end: embeddings, chunking, hybrid search, reranking, and vector databases.
• Experience with LLM evaluation and guardrails: building eval sets and harnesses, LLM-as-judge scoring, regression gating, and output-quality and safety checks.
• Experience instrumenting LLM systems for observability: tracing model and tool calls, versioning prompts, and using traces to debug and improve real behavior.
• Comfort owning ambiguous systems end to end in a fast-moving early-stage environment.
• Stateful agent-orchestration frameworks such as LangGraph or AutoGen, and durable-execution engines such as Temporal for long-running workflows.
• Experience building MCP tools or servers, or similar tool-calling integration layers.
• LLMOps and evaluation tooling such as MLflow or Langfuse for tracing, prompt versioning, and evaluation.
• Human-in-the-loop and interrupt-driven agent patterns for review and control.
• Applying LLMs to scientific or technical workflows, grounding reasoning in tool outputs and structured data.
• Fluency with modern AI coding assistants, or open-source contributions to AI or agent tooling.