AI + Data Engineer

Job Summary


Responsibilities

• Build and ship GenAI applications: RAG, agents and LLM-powered features using commercial and open-source models
• Build, train and evaluate ML and deep learning models for problems such as NLP, computer vision and forecasting
• Test model and LLM outputs and put guardrails in place before release
• Deploy and monitor models and AI services using APIs, containers and CI/CD
• Build and maintain the data pipelines and data models your solutions depend on
• Work with clients to turn business problems into well-scoped solutions, and explain trade-offs clearly

Requirements

• 3+ years of hands-on experience building AI/ML solutions
• Strong Python and Git, with clean, tested code
• GenAI: LLM APIs (OpenAI, Anthropic, Gemini or open-source models), RAG with vector databases, agent frameworks (LangGraph, LlamaIndex or similar), prompt design and evaluation
• ML and deep learning: scikit-learn, gradient boosting, PyTorch or TensorFlow
• Deployment: FastAPI, Docker, MLflow or similar, on at least one cloud (AWS, Azure or GCP)
• Good SQL and an understanding of data modelling (schema design)
• Clear written and verbal communication, and ownership of your work

Good to have

• Data engineering: PySpark, Airflow or similar orchestration, data warehouses or lakehouses
• Databricks experience or certification
• Fine-tuning open-source models (LoRA / QLoRA)
• Computer vision or multimodal models in production
• A public GitHub, blog or open-source contributions. Show us your work!

Qualifications

• Bachelors or Masters degree in Computer Science, Data Science, AI or a related field
• Certifications in Databricks, cloud or GenAI are a plus

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.