AI Engineer

We are looking to hire passionate AI Engineers to help turn data into intelligent production-ready solutions. You will work across the full AI stack: traditional machine-learning models large language models (LLMs) computer-vision pipelines and analytics / forecasting workflows. If you enjoy exploring data building state-of-the-art models and shipping reliable AI services we would love to meet you.

Responsibilities

• Model Development Design train fine-tune and evaluate models spanning classical ML deep learning (CNNs transformers) and generative AI (LLMs diffusion).
• Data Exploration & Analytics Conduct exploratory data analysis statistical testing and time-series / forecasting to inform features prompts and business KPIs.
• End-to-End Pipelines Build reproducible workflows for data ingestion feature engineering / prompt stores training CI/CD and automated monitoring.
• LLM & Agentic AI Engineering Craft prompts retrieval-augmented generation (RAG) pipelines and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements.
• AI Automation & Integration Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow Prefect) and business APIs to automate decision pipelines.
• Continuous Learning Track advances in LLMs vision and analytics; share insights and best practices with the wider engineering team.

Requirements

• BSc in Computer Science Mathematics or related field.
• Up to 5 years combined academic internship or professional experience on AI/ML projects.
• Proficient in Python and core libraries (PyTorch / TensorFlow scikit-learn pandas NumPy).
• Solid understanding of machine-learning algorithms deep-learning fundamentals and basic statistics.
• Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis.
• Familiarity with at least one of: OpenCV Hugging Face Transformers LangChain MLflow or similar.
• Good grasp of software-engineering best practices: Git code reviews testing CI.

Preferred Qualifications

• Knowledge of C or C# for performance-critical modules.
• Experience deploying models via Docker Kubernetes or cloud AI services.
• Exposure to vector databases and RAG workflows.
• Skill in BI / dashboard tools (Power BI Tableau Streamlit) or time-series frameworks (Prophet statsmodels).
• Familiarity with MLOps / LLMOps tooling (DVC MLflow Tracking Weights & Biases BentoML).
• Experience with image processing techniques (e.g. OpenCV image segmentation feature extraction)