As an AI Engineer, you’ll play a key role in building, deploying, and maintaining scalable AI solutions that power data-driven decision-making and customer experiences. You’ll work closely with Data Scientists, Data Engineers to bring machine learning models and large language models (LLMs) from research to production — ensuring they are reliable, efficient, and impactful.

Key Responsibilities

1. Model Development & Integration

• Collaborate with Data Scientists to implement, fine-tune, and optimize ML and AI models for production.
• Support feature engineering, data preprocessing, and dataset management.
• Integrate AI models into business systems and customer-facing applications.

2. Infrastructure & MLOps

• Design and maintain CI/CD pipelines for model training, testing, and deployment.
• Use Docker, Kubernetes to deploy and scale AI services.
• Build and manage end-to-end ML pipelines using orchestration tools such as MLFlow or Kubeflow.
• Monitor model and system performance, ensuring uptime, scalability, and reliability.

3. Vector Databases & LLM Applications

• Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases such as Qdrant, Pinecone, or Weaviate.
• Integrate LLM-based solutions (e.g., chatbots, assistants, summarization tools) into Snapp Market’s products.
• Experiment with embeddings, prompt engineering, and model fine-tuning for business use cases.

4. Collaboration & Documentation

• Partner cross-functionally with Data Science, Engineering, and Product teams.
• Document model architectures, workflows, and deployment procedures for reproducibility.
• Contribute to internal AI best practices, standards, and tooling improvements.

5. Innovation & Continuous Learning

• Stay ahead of emerging AI and MLOps technologies, frameworks, and research.
• Prototype new AI solutions and assess their business value.
• Promote a culture of experimentation, learning, and technical excellence.

What You’ll Bring

• Strong programming skills in Python.
• Hands-on experience with PyTorch, TensorFlow, or Hugging Face Transformers.
• Solid understanding of MLOps, CI/CD.
• Experience with Docker, Kubernetes, and model orchestration tools.
• Familiarity with vector databases and retrieval-based AI systems.
• Experience working with or deploying LLMs in production.
• Understanding of APIs, data pipelines, and system integration.
• Excellent problem-solving, analytical, and collaboration skills.