Description :

Role Overview :

The AI/ML Architect will be responsible for designing, implementing, and governing enterprise-scale AI systems in a highly regulated financial and cybersecurity-sensitive environment.

This role requires expertise in AI architecture, financial systems, data governance, and regulatory compliance, ensuring solutions are secure, explainable, auditable, and aligned with policy frameworks.

Key Responsibilities :

1. AI Architecture & Solution Design

Design scalable AI/ML systems for :

- Fraud detection & anomaly detection

- Risk analytics (credit, market, liquidity)

- Regulatory reporting automation

- Predictive analytics on financial data

- Build architectures for real-time and batch processing systems

2. AI Governance & Model Risk Management

Establish frameworks for :

- Model validation and lifecycle governance

- Explainable AI (XAI)

- Bias detection and fairness

Ensure all models are :

- Auditable

- Traceable

- Compliant with internal and regulatory standards

3. Secure MLOps & Deployment :

Design and implement secure MLOps pipelines with :

- Model versioning and lineage

- CI/CD pipelines with approval workflows

- Automated monitoring and retraining

- Ensure end-to-end auditability of model lifecycle

4. Data Architecture & Governance :

Define and implement :

- Secure data pipelines and storage

- Data quality, lineage, and cataloging frameworks

Ensure :

- Data privacy compliance

- Encryption, access controls, and data masking

- Work with structured and unstructured financial datasets

5. AI for Cybersecurity & Threat Intelligence

Architect AI-driven solutions for :

- Threat detection and behavioral analytics

- Fraud prevention systems

- Network and transaction anomaly detection

- Integrate AI with security monitoring systems (SOC/SIEM)

6. Financial Domain Alignment :

- Translate financial and regulatory requirements into AI solutions

Work on :

- AML/KYC analytics

- Transaction monitoring systems

- Early warning indicators for systemic risk

7. Cloud & Infrastructure Architecture :

Design hybrid and secure architectures across :

- Amazon Web Services

- Microsoft Azure

- Google Cloud Platform

Optimize for :

- Security

- Scalability

- Cost efficiency

8. Stakeholder Collaboration :

Work with :

- Business and risk teams

- Cybersecurity and compliance units

- Data engineering and IT teams

Convert complex business/regulatory problems into AI-driven solutions :

9. Leadership & Mentorship :

Lead cross-functional teams :

1. Data Scientists

2. ML Engineers

3. Data Engineers

- Establish best practices, reusable frameworks, and coding standards

Technical Skills Required :

AI/ML Expertise :

Strong foundation in :

- Machine Learning & Deep Learning

- NLP, time-series forecasting, anomaly detection

- Graph analytics (fraud detection networks)

Frameworks :

- TensorFlow

- PyTorch

Programming & Tools :

- Python (mandatory)

- Libraries: Scikit-learn, Pandas, NumPy

- API development (FastAPI/Flask)

MLOps & DevSecOps :

Tools :

- MLflow

- Kubeflow

- Docker

- Kubernetes

Experience in secure CI/CD pipelines :

- Data & Big Data:

1. Apache Spark, Kafka

2. SQL / NoSQL databases

- Data warehousing and streaming architectures

Cybersecurity & Compliance :

Familiarity with :

- ISO 27001

- NIST frameworks

- Data protection and privacy regulations

- Understanding of secure architecture principles

Domain Expertise (Critical) :

Strong understanding of :

- Banking and financial systems

- Risk and compliance frameworks

- AML/KYC processes

- Experience working in regulated or compliance-driven environments

Qualifications :

- Bachelors/Masters in Computer Science / AI / Data Science

Preferred :

- Advanced degree (PhD or equivalent experience)

Certifications :

- Cloud certifications (AWS/Azure/GCP)

- Security certifications (CISSP, CISM good to have)

Experience Requirements :

- 12+ years in technology / data roles

- 5+ years in AI/ML architecture or lead roles

Experience in :

- BFSI / FinTech / regulatory ecosystems

- Large-scale, secure data environments

Key Competencies :

- Strong architectural and systems thinking

- Deep understanding of AI governance and compliance

Ability to balance :

- Innovation

- Risk management

- Strong stakeholder communication skills

KPIs / Success Metrics :

- Model accuracy and reliability

- Compliance and audit readiness

- Reduction in fraud/anomaly detection time

- Production deployment success rate

- AI adoption across business functions

Ideal Candidate Profile :

- Experience in AI + Financial Systems + Compliance intersection

- Has deployed production-grade AI systems in regulated environments

Strong focus on :