AIML Architect

Job Summary:

Machine Learning Architect

end-to-end machine learning platforms and pipelines

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.

• 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.