Job Responsibilities

Direct Responsibilities

- End‑to‑End Solution Ownership – Lead the full project lifecycle: engage business stakeholders, translate requirements, architect solutions, develop, deploy, and provide ongoing support for AI‑powered cyber‑ and fraud‑detection tools.
- Advanced Model Development – Design, train, fine‑tune, and evaluate machine‑learning and deep‑learning models (especially transformer‑based and Large Language Models) with PyTorch, TensorFlow or JAX.
- GenAI / Agentic Systems – Research, build, and operationalize GenAI solutions using LLMs, small language models (SLMs), and agentic frameworks such as LangGraph or LangChain.
- Prompt Engineering – Create sophisticated prompts, context chains, and input/output pipelines to maximize LLM performance for security/fraud use cases.
- Data Engineering – Develop secure ETL pipelines; handle large, sensitive structured and unstructured data sets for model training, inference and monitoring.
- User‑Interface Development (optional/plus) – Produce simple, business‑friendly UIs (e.g., Streamlit, Gradio, Flask) that enable teams to interact with AI solutions seamlessly.

Contributing & Collaboration

- Partner with SMEs and business users to uncover opportunities for GenAI/agentic solutions.
- Keep abreast of the latest LLM/SLM and AI‑for‑cybersecurity research and inject relevant advances into the organization.
- Drive AI upskilling, share best practices, and mentor CDF teams.
- Document architectures, design decisions, and provide technical guidance/training to stakeholders.

Required Education, Skills and Experience

Category Requirements

Education

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent practical experience).

Skilled Experience





- 4 + years hands‑on ML/DL in production environments (cyber‑security domain experience is a plus).
- Demonstrated ability to take a project from ideation through POC, MVP, and production deployment.

Core AI/ML Skills

- Deep expertise in machine‑learning and deep‑learning, especially transformer models (BERT, GPT, Llama, Mistral, …).
- Hands‑on experience with LLMs/SLMs: architecture, pre‑training, instruction tuning, supervised/reinforcement fine‑tuning, quantization, and rigorous evaluation.
- Strong foundation in GenAI, NLP, and prompt engineering.

Programming & Data Engineering

- Proficient in Python and key ML libraries (PyTorch/TensorFlow, pandas, scikit‑learn).
- Solid knowledge of ETL processes, data wrangling, and building/maintaining production data pipelines.

Solution Ownership & Collaboration

- Proven track record of delivering end‑to‑end AI solutions.
- Effective communication with business stakeholders, SMEs, and cross‑functional teams.
- Ability to document solutions and provide technical training.

Behavioral Competencies

- Analytical mindset, proactive learning, and high‑quality delivery in regulated environments.

Preferred Education, Skills and Experience

- Agentic & Orchestration Frameworks – Experience with LangGraph, LangChain, or comparable workflow tools.
- User‑Interface Development – Practical use of Streamlit, Gradio, Flask, or similar frameworks to build interactive UI layers.
- DevOps & Cloud Deployment – Familiarity with Docker, REST APIs, and cloud‑native ML platforms (GCP, Azure, AWS).
- Cybersecurity Domain Knowledge – Direct experience applying AI/ML to cyber‑risk, fraud detection, or security operations.
- Secure AI Deployment – Understanding of best practices for robust, compliant AI deployments in regulated settings.
- Portfolio / Public Code – Accessible GitHub repositories, case studies, or project portfolios showcasing relevant GenAI, LLM, or agentic solutions.

📌 AI Solutions Developer (Chennai)
🏢 BNP Paribas
📍 Chennai