Junior AI engineer

Job Description

Job Description

Salary:

Role Overview

Green Ocean Open Management is hiring a Junior AI Engineer to join our core AI & Quantitative Engineering division. You will help engineer and optimize ML systems for high-frequency financial prediction, risk analytics, and algorithmic decision support. The role requires strong data-centric programming skills, an understanding of ML/DL fundamentals, and an appetite to apply AI in capital markets and sustainable investing frameworks.

Core Responsibilities

• Implement and maintain ML pipelines for financial time-series modeling, credit scoring, sentiment analysis, and alpha signal generation.
• Develop and validate predictive models using libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Support feature engineering and model tuning using structured market data, alternative data sources, and NLP-extracted signals.
• Integrate model outputs with real-time financial dashboards or algorithmic trading engines.
• Work closely with data engineers and quant analysts to ensure reproducibility and scalability of models across backtesting and production environments.
• Perform error analysis and iterate on model robustness, fairness, and performance monitoring.

Required Technical Skills

• Solid programming foundation in Python; hands-on with Pandas, NumPy, matplotlib, joblib, etc.
• Experience in implementing supervised/unsupervised ML algorithms (logistic regression, decision trees, clustering, PCA, etc.).
• Understanding of time-series modeling concepts: stationarity, lag features, moving averages, autocorrelation.
• Exposure to version control systems like Git, and use of Jupyter, VS Code, or Colab for research workflows.
• Familiarity with SQL-based data retrieval and ETL operations for structured financial datasets.

Preferred (Bonus) Qualifications

• Familiarity with LLMs, prompt engineering, or APIs from OpenAI/HuggingFace (e.g., for financial document QA or semantic analysis).
• Understanding of financial instruments (equities, derivatives, fixed income), market microstructure, or portfolio theory.
• Experience with model deployment (Docker, FastAPI, Flask) or monitoring (Weights & Biases, MLflow).
• Cloud experience (AWS/GCP/Azure) for scalable model training or data warehousing.

What Youll Gain

• Hands-on work with production-grade financial ML systems under the guidance of quant and AI leads.
• Opportunity to work on green finance use cases, sustainable investing algorithms, and ESG-oriented NLP models.
• Cross-functional exposure across AI research, financial engineering, and operational deployment.
• Culture of continuous learning, internal research sprints, and access to cloud GPU environments.