Offer summary

Qualifications:

3+ years of experience in AI/ML engineering or LLM-based systems., Hands-on experience with LangChain, LangFlow, or similar orchestration tools., Proficient in Python and ML frameworks like PyTorch or TensorFlow., Familiarity with Docker, Kubernetes, and CI/CD systems..

Key responsibilities:

Design and deploy AI agent workflows using LangChain and LangFlow.

Implement Model Context Protocol (MCP) for agent-tool-data interactions.

Build Agent-to-Agent systems for task automation across various domains.

Collaborate with Data Engineering to ensure scalable and secure data pipelines.

Job description

🤖 AI Engineer – Agent-Oriented LLM Workflows

We’re building the future of AI-driven agent workflows—and we want you to help lead the way. As an AI Engineer, you'll architect, deploy, and optimize advanced LLM-based agent systems that can interact, reason, and deliver business value at scale.

You’ll collaborate closely with data engineers and product stakeholders to bring multi-agent orchestration frameworks, retrieval-augmented generation (RAG) pipelines, and real-world deployment strategies to life.

🗓 Start date: ASAP
📆 Contract type: Contractor - Indefinite
🌐 Work hours: Monday to Friday, 7.30 am to 4.30 pm PST - 100% Remote

🛠️ What You’ll Be Doing

Design and deploy AI agent workflows using LangChain and LangFlow.
Implement MCP (Model Context Protocol) to standardize agent-tool-data interactions.
Build Agent-to-Agent (A2A) systems for orchestrated task automation across domains (e.g., reporting, validation, marketing agents).
Define production-ready agentic pipelines with robust logging and resilience.
Benchmark and select the most suitable LLMs (GPT-4, Claude, LLaMA) based on latency, cost, and task complexity.
Design RAG architectures using vector databases to enhance response quality and domain alignment.
Optimize prompts, model parameters, and outputs for consistency and accuracy.
Containerize and deploy agents using Docker and Kubernetes.
Set up real-time monitoring and performance evaluation dashboards for agent behavior and LLM output validation.
Collaborate on CI/CD pipelines and implement production guardrails.
Work closely with Data Engineering to ensure scalable and secure data pipelines for agents.
Lead architectural discussions and share best practices in agent orchestration.
Document workflows, configurations, and operational standards for internal teams.

✅ What You Need to Succeed

Must-haves

3+ years of experience in AI/ML engineering or LLM-based systems.
Hands-on experience in production with:
- LangChain, LangFlow, or similar orchestration tools
- Vector databases (e.g., Pinecone, Weaviate, FAISS)
- Python + ML frameworks (e.g., PyTorch, TensorFlow)
- Docker, Kubernetes, and CI/CD systems (GitHub Actions, Jenkins)
Proven experience deploying agentic systems and building pipelines involving LLMs.
Strong understanding of LLM prompt engineering, context management, and tool/agent interoperability.
Comfortable with Linux environments and cloud platforms (AWS/GCP/Azure).

Nice-to-haves

Experience with LangGraph, AutoGen, CrewAI, or other multi-agent orchestration frameworks.
Prior work on chatbots, autonomous agents, or RAG pipelines.
Familiarity with AI security, compliance, or ethical risk mitigation.
Contributions to open-source AI projects or academic publications.