Sr/ Expert, AI Engineer/ Data Scientist - TNEX - 4B063

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