AI/ML Engineer

About the role

Emporix is a next-generation Autonomous Commerce Intelligence platform, built for modern B2B and sophisticated B2C enterprises. We uniquely combine orchestration, automation, and AI to streamline commerce processes across systems. We’re looking for a skilled AI Python Developer / Machine Learning Engineer to join our team. You’ll work on cutting-edge backend systems and AI models, leveraging large and small language models (LLMs/SLMs), retrieval-augmented generation (RAG), and agentic frameworks to power intelligent, production-ready commerce solutions.

If you're excited by scalable AI systems, LLM fine-tuning, modern MLOps, and working in a cloud-native environment—this role is for you.

Requirements

• 3+ years of hands-on experience in Python development, ideally in AI, ML, or backend systems.
• Proven experience with LLM/SLM fine-tuning and deployment (e.g., LoRA, QLoRA).
• Hands-on experience with agentic AI frameworks like LangChain, LangGraph, or FastMCP.
• Strong knowledge of transformer models and libraries like Hugging Face, Mistral, LLaMA, or Gemma.
• Familiarity with vector databases, semantic search, and prompt engineering.
• Experience designing and deploying RAG pipelines in real-world applications.
• Skilled in building and maintaining event-driven microservices and async APIs.
• Cloud-native engineering experience, preferably on GCP (Cloud Run, Pub/Sub, Storage).
• Familiarity with secure coding practices and data protection in AI systems.
• Strong communication skills in English (B2/C1 level).
• Bonus: Experience with Go, Java, or orchestrating cloud functions.

Responsibilities

AI & Backend Development

• Design and build intelligent backend services using Python and FastAPI.
• Develop agent-based systems using LangChain, LangGraph, and FastMCP.
• Integrate LLMs, RAG pipelines, and custom tools into microservices architecture.
• Implement orchestration logic for multi-agent workflows and real-time SSE-based interactions.

LLM/SLM Model Engineering

• Fine-tune and optimize LLMs/SLMs on domain-specific datasets.
• Develop custom models tailored to business use cases and workflows.
• Evaluate models for performance, latency, cost-efficiency, and robustness in production.

Production-Grade MLOps & Cloud Deployment

• Deploy and monitor models and services on Google Cloud Platform (GCP) using Kubernetes, Cloud Run, and Pub/Sub.
• Build reproducible training and inference pipelines with CI/CD and MLOps best practices.
• Track performance with logging, monitoring, and feedback loops for continuous improvement.

System Integration & Collaboration

• Work closely with frontend, backend, and product teams to deliver scalable, production-ready AI systems.
• Ensure event-driven, fault-tolerant communication between AI modules and commerce services.

Documentation & Knowledge Sharing

• Maintain clean and well-structured documentation for APIs, ML pipelines, and system logic.
• Contribute to knowledge bases and architecture diagrams for long-term scalability and onboarding.