Sr/Expert AI Engineer (Data Scientist, Python) - 4B063

Mô tả công việc

We are building customer-facing and internal AI products that directly impact growth, risk, operations, and customer experience in a regulated fintech environment.

As a Senior / Expert AI Platform Engineer, you will lead the delivery of LLM-powered products such as chatbots, voicebots, and analytics co-pilots, while ensuring they are production-grade, compliant, and measurable.

This is a product-heavy role: success is defined by business outcomes, not just infrastructure.

You will work end-to-end across data → model → product → monitoring, partnering closely with Product, Risk, Compliance, and Engineering teams.

What Youʼll Build:

Customer-facing AI used by millions of users, AI assistants embedded in critical fintech workflows, A trusted, compliant AI platform adopted across the company, Measurable business impact across growth, cost, and risk

Why Join Us:

Build real AI products, not demos, Direct ownership of business-critical systems, Strong executive sponsorship for AI & GenAI, Modern stack: AWS Bedrock, dbt, Airflow, MLflow, High trust, high impact, expert-level autonomy

Main Responsibilities include:

1. AI Products & Customer Journeys

• Own the design and implementation of AI-driven products
  • Customer service chatbots & voicebots
  • Internal analytics & decisioning co-pilots
  • AI assistants for operations, sales, and risk teams
• Translate business problems (conversion, cost-to-serve, fraud, credit decisioning, CX) into AI solutions
• Design LLM workflows using RAG, agents, tools, and structured reasoning
• Integrate models via AWS Bedrock with strict controls on latency, cost, and reliability

2. Fintech-Grade AI & ML Platform

• Build and evolve a shared AI & ML platform supporting:
  • Predictive ML (risk, fraud, personalization)
  • LLM & GenAI workloads
• Productionize models with strong MLOps practices:
  • Experiment tracking & model registry (MLflow)
  • CI/CD for models, prompts, and agents
• Orchestrate data & ML pipelines using Airflow
• Enable analytics, features, and training datasets using dbt
• Support both real-time inference and batch decisioning

3. Risk, Governance & Compliance (Critical)

• Design AI systems aligned with fintech governance requirements:
  • Model versioning, traceability, and auditability
  • Explainability and decision logging where required
• Implement guardrails for:
  • Hallucination control & safe completion
  • Data privacy & PII protection
  • Model misuse and prompt injection
• Partner with Risk, Compliance, and Legal to enable safe GenAI adoption
• Support internal reviews, validations, and regulator-ready documentation

4. Monitoring, Quality & Continuous Improvement

• Define and operate AI observability:
  • Model & prompt performance
  • Drift, bias, and degradation
  • Latency & cost efficiency
• Build feedback loops from users, agents, and business teams
• Continuously improve prompts, models, and product experience