SENIOR AGENTIC AI ENGINEER

About This Job

OmniPhi is building the first Integrated Agentic Trading Environment (IATE), bringing strategy research, development, testing, deployment, and live operation into one AI-native platform.

We are hiring a Senior Agentic AI Engineer to improve our core agentic systems, including the agent harness, tool use, memory, context management, RAG, multi-agent coordination, reliability, and efficiency.

This is a hands-on engineering role requiring experience building and improving production agentic systems beyond basic chatbots and API wrappers.

Responsibilities

• Design, build, and continuously improve the core agent harness.
• Develop reliable tool creation and tool-calling systems across internal tools, external APIs, databases, and MCP connections.
• Implement structured outputs, tool schemas, validation, permissions, retries, and failure recovery.
• Build persistent sessions, agent memory, state management, delegation, and multi-agent workflows.
• Develop RAG systems using ingestion pipelines, embeddings, vector and hybrid search, reranking, and citations.
• Optimize prompts, context assembly, token efficiency, caching, model routing, latency, and inference costs.
• Build evaluations, tracing, monitoring, guardrails, and regression testing for agent behavior and tool execution.
• Integrate commercial and open-weight language models into reliable production services.

Required Qualifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field, or equivalent practical experience.
• 5+ years of software engineering, machine learning, or AI engineering experience.
• Production experience building agent harnesses, agent runtimes, or complex LLM applications.
• Advanced Python skills, including asynchronous programming, APIs, testing, and production debugging.
• Strong Rust skills, with the ability to build, maintain, and contribute to production Rust services.
• Strong understanding of prompt engineering, context engineering, tool calling, MCP, and structured outputs.
• Experience with RAG, agent memory, embeddings, vector databases, and retrieval optimization.
• Experience with LangChain, LangGraph, LlamaIndex, PydanticAI, or comparable technologies.
• Experience evaluating and improving agent quality, reliability, latency, token usage, and cost.

Strongly Preferred

• Familiarity with PyTorch, TensorFlow, Hugging Face, ONNX, or vLLM.
• Fine-tuning experience using LoRA, QLoRA, PEFT, or comparable techniques.
• Knowledge of quantization, distillation, and efficient model inference.
• Understanding of mixture-of-experts architectures and model-routing systems.
• Knowledge of machine learning, deep learning, and natural language processing fundamentals.
• Experience with SQL, PostgreSQL/pgvector, Redis, data pipelines, cloud services, and Linux.
• Experience with coding agents, sandboxed execution, or durable workflow systems.
• Familiarity with TypeScript, Java, or Bash is an asset.

What We’re Looking For

You do not need experience with every framework or technology listed above.

We are looking for someone who understands the underlying architecture of modern agentic systems.

You should have personally built or made substantial contributions to meaningful production agent systems.

You should be able to clearly explain your architectural choices, technical decisions, and trade-offs.

Work Location

Hybrid remote in Vancouver, BC