AI Engineer

Job Title: AI Engineer

Job Type: Client FTE

Role Overview:

As an AI Engineer, you will play a crucial role in developing and deploying AI-powered solutions that enhance banking products. This is a hands-on position where youll collaborate with cross-functional teams to design, implement, and optimize machine learning models for real-world applications.

Key Responsibilities:

AI/ML Model Development:

• Design, develop, and train machine learning models for key banking applications (e.g., fraud detection, credit scoring, personalized financial advice, customer service automation).
• Fine-tune pre-trained models and build custom AI solutions as needed
• Build Knowledge Index and fine tune LLMs with it

Data Preparation and Analysis:

• Work with structured and unstructured financial datasets to preprocess, clean, and analyze data for AI/ML applications.
• Implement feature engineering techniques to enhance model performance.
  

Model Deployment:

• Deploy AI/ML/LLM models into production environments using MLOps best practices.
• Monitor, maintain, and improve model performance post-deployment.

Collaboration:

• Collaborate with product managers, software engineers, and domain experts to align AI capabilities with business requirements.
• Contribute to brainstorming sessions for innovative AI solutions.

Documentation and Reporting:

• Document workflows, models, and results for internal use and audits.
• Provide regular updates on project progress and insights.

Skills and Qualifications:

Required:

• Bachelors degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 4+ year of experience in AI/ML development (internships and projects count).
• Experience with NLP models (e.g., BERT, GPT) for text-based banking applications.
• Strong programming skills in Python, with experience in AI/ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with data processing frameworks like Pandas and NumPy.
• Solid understanding of machine learning concepts, including supervised and unsupervised learning.
• Familiarity with version control tools (e.g., Git) and cloud platforms (AWS, GCP, or Azure).
• Strong problem-solving skills and a startup mindset.

Preferred:

• Knowledge of time-series modeling for financial data.
• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, or Docker).
• Background in fintech or banking is a plus.
• Understanding of cybersecurity and regulatory compliance in banking.