Mid & Senior AI Engineer (Agentic AI & LLM Systems)
Summary
Build and deploy LLM-driven AI agents and chatbots using Python and Docker, from architecture to production monitoring.
About the Role
We are looking to hire both a Mid‑Level AI Engineer and a Senior AI Engineer to join our client's growing technical team.
In these roles, you will design, build, and deploy intelligent AI systems, with a core focus on Agentic AI, LLM‑driven architectures, and conversational AI (including Claude AI). You will handle the full lifecycle of AI applications, from defining system architecture and complete process flows to Docker‑based deployment and performance monitoring.
Note: Responsibilities and architectural ownership will scale based on the level (Mid vs. Senior) you are hired into.
Key Responsibilities
For Mid-Level AI Engineers:
Development: Write clean, efficient, and scalable Python code to build LLM applications, custom agents, and chatbot features.
Process Flow Integration: Help implement end‑to‑end data pipelines, vector search, and agentic workflows under guided architecture.
Deployment & Containerization: Use Docker to containerize applications and assist with production deployments.
For Senior AI Engineers:
System Architecture: Lead the design and implementation of end‑to‑end process flows, system architectures, and multi‑agent AI ecosystems.
Production Deployment: Own the deployment, hosting, and monitoring pipelines for large language models and complex agentic systems.
Technical Leadership: Make core technical decisions, review code, and ensure high system reliability, low latency, and scalable performance.
Requirements
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Experience:
Mid‑Level Role: Minimum 2+ years of hands‑on experience developing and deploying AI/LLM applications.
Senior Role: 4+ years of professional experience in software engineering, with a strong track record of leading AI system design and production deployments.
Core Tech Stack: Advanced proficiency in Python and solid hands‑on experience with Docker.
AI & LLM Domain: Proven experience working with LLMs (e.g., Claude AI), Agentic AI concepts, and chatbot architectures.
End‑to‑End Delivery: Demonstrated experience taking AI projects across the entire process flow—from design to production deployment.
Nice to Have
Experience with orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
Hands‑on experience with cloud infrastructure (AWS, GCP, or Azure).
Experience with vector databases and model fine‑tuning techniques.