Artificial Intelligence Engineer

Why This Role is Special

• Greenfield + Impact:

  Build GenAI-powered financial solutions from scratch with minimal legacy baggage.

• Enterprise-grade Scale:

  Your models will power risk, fraud, and personalization systems used in high-stakes fintech environments.

• Innovation at Core:

  From RAG pipelines to fine-tuned LLMs, you’ll explore and deploy the latest AI techniques.

• End-to-End Ownership:

  From idea to production—data pipelines, modeling, compliance, and observability—you’ll drive the full cycle.

Key Responsibilities:

• AI/ML Development:

  Architect and implement advanced ML systems for fraud detection, credit scoring, personalization, and risk modeling.

• Generative AI Solutions:

  Build and deploy LLM-powered tools for financial document summarization, chatbot automation, KYC workflows, and customer support.

• LLM Experimentation:

  Work with OpenAI, Claude, LLaMA, Mistral, etc.—covering prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG).

• MLOps & Deployment:

  Own CI/CD for models, scalable serving, monitoring, and explainability (using MLflow, Kubeflow, or GCP/AWS/Azure ML stacks).

• Cross-Functional Collaboration:

  Partner with product, compliance, and engineering teams to embed AI responsibly into fintech journeys.

• Model Governance:

  Ensure fairness, bias mitigation, explainability (XAI), and adherence to financial regulatory standards.

• Team Leadership:

  Mentor junior engineers and data scientists, fostering a high-performance AI culture.

Preferred Qualifications

• Prior experience with fintech AI use-cases (fraud, risk, credit scoring, personalization).
• Exposure to LangChain, Hugging Face, OpenAI APIs.
• Experience with MLOps stacks (MLflow, SageMaker, Vertex AI, Kubeflow).
• Contributions to AI research, open-source projects, or technical blogs/papers.

What Success Looks Like in 6 Months

• Delivered at least 2 production-grade AI/GenAI systems impacting customer experience or financial operations.
• Established scalable, compliant, and monitored ML pipelines.
• Built a strong AI/ML engineering foundation for Ambak.com’s long-term roadmap.
• Actively mentored team members and elevated AI culture across the org.