Your role & responsibilities
As a Machine Learning Engineer, you will:
- Design, develop, and deploy ML and NLP models to solve business problems in areas like customer service, compliance, fraud, and operational efficiency.
- Build and optimize retrieval-augmented generation (RAG) pipelines integrating LLMs and knowledge bases.
- Develop and enhance chatbots and virtual assistants using GenAI and conversational AI frameworks.
- Collaborate with cross-functional teams to deliver AI products on Microsoft Azure cloud infrastructure.
- Automate end-to-end ML workflows using tools like Azure ML, Databricks, and MLOps pipelines.
- Ensure models are secure, ethical, explainable, and compliant with financial industry regulations.
- Contribute to continuous learning, experimentation, and evaluation of new GenAI tools, open-source frameworks, and model architectures.
Key qualifications
Must-haves
- 3+ years of experience in Machine Learning, with a focus on NLP or AI applications
- Strong Python programming skills (e.g., scikit-learn, PyTorch, Transformers, Langchain)
- Solid experience working with Azure cloud services (Azure ML, Azure Functions, Azure DevOps, etc.)
- Hands-on experience with LLMs, RAG frameworks, vector databases, and embedding models
- Background in building or scaling chatbots, Q&A systems, or conversational AI
- Familiarity with data engineering principles and MLOps practices
- Good understanding of model evaluation, bias detection, and explainability in ML systems
Nice-to-have
- Experience with tools like Openai API, Hugging Face, Langchain, Weaviate, Pinecone, or Qdrant
- Knowledge of regulatory or compliance frameworks in banking (e.g., GDPR, data privacy)
- Previous experience in the financial services or banking industry
Soft skills
- Strong analytical and problem-solving skills
- Clear and effective communicator, both with technical teams and business stakeholders
- Passion for experimentation and emerging AI trends
- A team player with a proactive and solution-driven mindset
