AI Solutions Developer

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).

Professional 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.