AI Engineer to work on Agent Architecture and Orchestration

Role Overview

We are seeking an AI Engineer to focus on Agent Architecture and Orchestration to design and implement sophisticated agentic systems for B2B operational processes.

Responsibilities

• Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution for a B2b operational processes.
• Build stateful workflows using frameworks such as LangGraph and LangChain - including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.
• Engineer for long-horizon reliability - multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail.
• Develop end-to-end Retrieval-Augmented Generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
• Engineer memory and context management - conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.
• Apply modern context-delivery patterns (e.g., MCP-style tool/context interfaces) so agents access the right information at the right time.
• Implement observability and tracing for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behavior.
• Apply guardrails, safety controls, and failure-handling to reduce hallucinations and unsafe actions.
• Evaluate agents at the trajectory and task level - multi-step task success, failure-mode and regression analysis, and sandboxed test environments - alongside retrieval- and generation-quality metrics, automated checks, and human review.
• Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers so agents operate safely within real business workflows.
• Deliver production-quality code with strong practices in testing, CI/CD, logging, versioning, and documentation; make architecture decisions that balance quality, safety, latency, cost, and model risk.

Requirements

• Demonstrated depth building and shipping production agentic systems - this is your primary experience, not a recent exploration, strong software/ML fundamentals plus substantial, recent hands-on agentic work.
• Strong, hands-on experience building production agent systems with modern orchestration - LangGraph/LangChain or equivalent, including custom orchestration.
• Experience designing and optimizing end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation.
• Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
• Deep, practical understanding of LLM behavior - strengths, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs - and the evaluation methods used to measure them.
• Experience evaluating and debugging agent behavior - task-success and trajectory analysis, not just output quality.
• Strong Python engineering skills and modern software practices: testing, CI/CD, version control, and API integration; experience implementing observability, tracing, and debugging for LLM-based systems in production.
• Hands-on experience with at least one frontier model platform (e.g., Anthropic, Google, OpenAI) and/or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.

Preferred qualifications include experience with multi-agent systems, familiarity with vector databases (e.g., Pinecone, Weaviate, or Milvus), exposure to model adaptation techniques (e.g., LoRA or QLoRA), and understanding of traditional NLP concepts.