AIML Architect

Job Summary:

We are seeking an accomplished Machine Learning Architect with 12 to 18 years of experience to lead the design and implementation of robust, scalable, and production-grade ML systems in the financial services domain. The ideal candidate will have deep expertise across the entire ML lifecycle — from data ingestion and modeling to deployment, serving, and operationalization — and a strong understanding of enterprise-level architecture and compliance considerations in the financial industry.

As an ML Architect, you will work cross-functionally with engineering, data science, product, and compliance teams to design end-to-end machine learning platforms and pipelines that power intelligent financial applications, such as fraud detection, credit scoring, customer analytics, and risk management.

Required Qualifications:

• 12 to 18 years of experience in software engineering, machine learning, or data platform architecture, with at least 5 years in architecting end-to-end ML solutions.
• Proven experience in the financial domain, working with use cases such as fraud detection, risk scoring, AML, churn prediction, or credit modeling.
• Deep knowledge of computer science fundamentals, including:
• Data structures and algorithms
• Distributed systems
• High-availability and low-latency system design
• Strong programming and architectural experience with Python, and at least one of Java/Scala/C++.
• Expertise in ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and MLOps platforms (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Experience building and deploying RESTful APIs or microservices to serve ML models.
• Solid hands-on experience with data processing tools (e.g., Apache Spark, Kafka, Airflow) and cloud infrastructure (AWS/GCP/Azure).
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes) for deploying scalable systems.

Preferred Qualifications:

• Prior experience in ML architecture within financial institutions, fintech, or regulatory environments.
• Working knowledge of governance and compliance frameworks: model auditability, explainable AI (XAI), fairness and bias detection.
• Experience designing real-time inference and streaming-based ML systems.
• Understanding of feature stores, model registries, and data lineage in ML pipelines.
• Strong communication and leadership skills, capable of influencing C-level stakeholders and guiding engineering decisions across departments.

Technical Skills

• 12+ years of experience in IT and relevant Machine learning experience

• Strong understanding of software engineering principles and fundamentals including data structures and algorithms.

• Excellent understanding of object-oriented concepts and Python.
• Strong knowledge of computer science fundamentals to develop a scalable system
• Experience in NLP models like BERT, Transformer architectures, etc.

• Experience in leveraging Computer Vision and OCR in document extraction use cases

• Familiarity with ML problems (ex, Classification/Regression/Anomaly Detection)
• Python ML Packages (Scikit/Numpy/Pandas/OpenCV)
• Exposure to REST API/ Flask concepts

• Experience in deep learning package, Pytorch, Tensorflow etc
• Familiarity with Graph database like Neo4j will be a plus.

Roles and Responsibilities

• Architect and design scalable ML systems that cover the entire lifecycle:
• Data acquisition and preprocessing
• Model training, validation, and optimization
• Model deployment and API serving
• Model monitoring, drift detection, retraining, and governance
• Build and maintain modular, reusable, and compliant ML platforms and services, ensuring scalability, reliability, and performance for production environments.
• Define architecture and best practices for:
• Model versioning and reproducibility
• Feature engineering and feature stores
• MLOps workflows (CI/CD pipelines, model registry, deployment automation)
• Collaborate with data engineers, ML engineers, software architects, and domain experts to align technical design with business goals and regulatory requirements.
• Ensure systems meet financial industry standards for data privacy, explainability, auditability, and regulatory compliance (e.g., model risk management frameworks).
• Mentor engineering and data science teams on architectural patterns, system design principles, and ML operationalization.
• Evaluate and integrate new technologies and tools in ML infrastructure and cloud platforms.