TNEX

Overview of job

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

1. Attractive Compensation and Rewards

• Competitive salary benchmarked against the market and aligned with your capabilities. The final salary package will be negotiated upon successfully passing the interview process.
• Year-end performance bonus ranging from 2 to 5 months' average salary, depending on individual performance evaluation results.

2. Comprehensive Benefits Package

• Statutory insurance coverage and additional private health insurance for employees.
• Diverse employee benefits, including flexible working hours, company trips, birthday leave, and more.
• Access to employee loan programs with preferential interest rates.

3. Learning, Development, and Career Advancement

• Structured and in-depth training programs focused on Re-skilling, Up-skilling, and New-skilling, tailored to specific job groups and career paths.
• Close mentoring and coaching from direct managers, senior leaders, and industry experts.
• Management development programs for high-potential employees, preparing them for future leadership and managerial roles.
• Priority is given to promoting internal talent into management positions, providing clear career progression opportunities.

4. Professional Work Environment and Dynamic Corporate Culture

• Friendly, supportive, and collaborative workplace that values accountability and mutual respect.
• Innovative and dynamic environment that encourages creativity, continuous learning, and improvement.
• Respect for diversity, with a strong emphasis on teamwork and effective collaboration.
• A wide range of cultural, sports, and recreational activities that foster employee engagement and a positive workplace atmosphere.

• Strong Python engineering skills
• Proven experience delivering AI/ML systems in production
• Hands-on experience with LLMs & GenAI (RAG, embeddings, evaluation)
• Experience using AWS Bedrock in real products
• Solid MLOps experience with MLflow Workflow orchestration with Airflow
• Experience with dbt and modern analytics/data stacks
• Strong product mindset: ability to connect AI outputs to business outcomes

Expert-Level Expectations

• Act as a technical authority for AI products and platforms
• Make architecture and design decisions with long-term scalability in mind
• Mentor senior engineers and influence AI best practices across teams
• Proactively identify high-impact AI use cases in fintech
• Balance innovation with risk, compliance, and operational stability

Nice to Have

• Experience in fintech, banking, or regulated industries
• Voicebot systems (STT/TTS, real-time streaming)
• Feature store or online decisioning systems
• Kubernetes or large-scale inference platforms
• Experience working with regulators, audits, or model risk teams