AI Machine Learning Engineer

4 weeks ago

Atlanta, Georgia, United States Enaar Group Full time
Key Responsibilities:

Risk Management & Fraud Detection:
Develop machine learning algorithms to enhance risk analysis, detect anomalies, and prevent financial crimes.

Predictive Models for Financial Markets:
Design and deploy machine learning models to predict market trends, identify investment opportunities, and assess financial risks.

Azure ML Implementation:
Utilize Azure Machine Learning services to build scalable and reliable ML workflows for financial applications.

Customer Personalization:
Use machine learning insights to deliver personalized financial products and investment recommendations for high-net-worth clients.

Regulatory Compliance:
Automate compliance processes using machine learning to ensure adherence to AML, KYC, GDPR, and CCPA regulations.

Quantitative Analysis & Trading:
Collaborate with quant teams to integrate machine learning models that optimize trading strategies and improve portfolio performance.

Data Processing & Analysis:
Preprocess and analyze large financial datasets to support accurate model development.

Stakeholder Collaboration:
Work with analysts, traders, portfolio managers, and compliance officers to align machine learning solutions with business needs.

Model Optimization:
Validate, retrain, and optimize machine learning models to ensure accuracy and adapt to changing market conditions.

Requirements:

Financial Knowledge:
Strong understanding of financial markets, risk analysis, portfolio management, and investment strategies.

Machine Learning Expertise:
Proficiency in supervised, unsupervised, and reinforcement learning algorithms for financial applications.

Programming & Tools:
Strong skills in Python, R, and experience with frameworks like TensorFlow, PyTorch, or scikit-learn.

Cloud Technologies:
Familiarity with cloud platforms such as Azure, AWS, or Google Cloud.

MLOps:
Experience in MLOps practices, with knowledge of tools such as Docker, Kubeflow, or MLflow.

Financial Data Handling:
Expertise in working with complex financial datasets, including market and transactional data.

Quantitative Finance:
Experience in time-series analysis, financial modeling, or trading strategies.

Education:
Bachelor's degree in Computer Science, Data Science, Financial Engineering, or a related field. Master's or Ph.D. preferred.

Experience:
4+ years of hands-on experience developing and deploying machine learning models, ideally in the financial services sector.

Certifications (preferred):
Certified Financial Risk Manager (FRM) or Chartered Financial Analyst (CFA) (Level I or higher).

Preferred Skills:

Quantitative Finance & Algo Trading:
Knowledge of trading strategies and financial algorithms.

Big Data Tools:
Experience with Apache Hadoop, Spark, or similar big data frameworks.

Visualization Tools:
Proficiency with data visualization tools like Tableau or Power BI.

Team Collaboration:
Ability to communicate effectively within cross-functional teams, translating complex technical concepts for both technical and non-technical stakeholders.

What We Offer:

Salary:
Competitive base salary of $164,000/year with performance-based bonuses.

Benefits:
Comprehensive health, dental, and vision plans, along with a 401(k) with company match.

Growth Opportunities:
Professional development support, training, and certification sponsorship.

Work-Life Balance:
Flexible hybrid work schedule to accommodate work-life balance.

Why Enaar Group:

Innovative Culture:
Join a firm dedicated to revolutionizing financial services with the most advanced AI and ML technology available.

High Impact Role:

Play a key role in building machine learning models that redefine risk management, enhance investment strategies, and improve client experiences.


Collaborative Environment:
Work with some of the best financial analysts, data scientists, and AI specialists in the industry.


Apply today and be part of an exciting journey where your expertise will shape the future of finance and impact high-net-worth clients in profound ways.

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