AI ML Developer

Role & responsibilities

Experience with NLP, deep learning, or time series analysis.

Experience deploying models to production environments.

Knowledge of regulatory requirements and compliance in banking and finance.

Familiarity with MLOps practices and tools.

Experience with Agile methodology and tools (JIRA or Rally)

Bachelors or Masters degree in Computer Science, Statistics, Mathematics, or a

related field.

Proven experience as a Data Scientist in Banking or a similar domain

Proficiency in Python or R, and experience with data science libraries (e.g., pandas,

scikit-learn, TensorFlow, PyTorch).

Hands-on experience with large language models (e.g., OpenAI GPT, Llama, or similar),

including fine-tuning and prompt engineering.

Strong knowledge of statistics, machine learning, and data mining techniques.

Experience with data visualization tools (e.g., Tableau, Power BI).

Preferred candidate profile

Experience with Big Data Platforms (Hadoop).

Familiarity with SQL and working with relational databases.

Excellent problem-solving, communication, and collaboration skills.

Experience with cloud platforms (AWS, Azure, or GCP) is a plus.

Analyze large financial datasets to extract insights and support business decisions.

Develop, implement, and evaluate machine learning models and algorithms tailored to

banking and finance use cases (e.g., risk modeling, fraud detection, customer

segmentation).

Apply and fine-tune large language models (LLMs) for tasks such as document analysis,

customer communication, and regulatory compliance.

Collaborate with cross-functional teams to understand business requirements and

deliver data-driven solutions.

Communicate findings and recommendations through reports, dashboards, and

presentations.

Work with data engineers to ensure data quality and pipeline reliability.