Key Responsibilities:
- Lead the design and implementation of generative AI use cases, from ideation to production.
- Build and tune LLM-based applications using platforms like Vertex, GPT, Hugging Face, etc.
- Create robust prompt engineering strategies and reusable prompt templates.
- Integrate generative AI with enterprise applications using APIs, knowledge graphs, vector databases (e.g., PG Vector, Pinecone, FAISS, Chroma), and orchestration tools.
- Collaborate with Tech and business teams to identify opportunities and build solutions.
- Responsible for model development, validation and testing of fraud detection models, including statistical analysis, data validation and model performance evaluation.
- Ensure compliance with regulatory requirements and industry best practices in fraud detection and model validation.
- Responsible for documenting the model development documents for MRM approvals and governance purposes.
- Stay current on GenAI trends, frameworks (LangChain, LlamaIndex), and research.
- Mentor junior team members and promote a culture of experimentation and learning.
- Stay abreast of advancements in Gen AI, LLMs, and fraud prevention technologies, applying relevant innovations to projects.
- Specialization in fraud or risk domain preferred
Qualifications:
- 11+ years of hands-on experience in data science, or AI/ML roles.
- 7+ years' experience in statistical analysis with working knowledge of at least one of the following statistical software packages: Python (Must), Hive, Spark, SAS Strong quantitative, analytical, and problem-solving skills; knowledge of probability theory, statistics, mathematical finance, econometrics, numerical methods
- 2+ years of hands-on experience specifically with generative AI or LLMs.
- Strong understanding of statistical concepts and modelling techniques including traditional and advanced ML algorithms
- Strong programming skills in Python, and familiarity with libraries like Transformers, LangChain, LlamaIndex, PyTorch, or TensorFlow.
- Working knowledge of retrieval-augmented generation (RAG) pipelines and vector databases.
- Understanding of MLOps / LLM Ops, model evaluation, prompt tuning, and deployment pipelines
- Experience building applications with OpenAI, Anthropic Claude, Google Gemini, or open-source LLMs.
- Familiarity with regulatory requirements and guidelines related to risk model validation
- Strong communication skills and the ability to partner with both technical and non-technical stakeholders.
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