Must Have Technical/Functional Skills
significant depth in AI/ML architecture and solution design on customer-facing programs
• Strong hands-on understanding of ML development frameworks and ecosystems
(e.g., Python + standard ML/DL libraries like scikit-learn, TensorFlow, PyTorch, HuggingFace)
• Proven architecture experience for cloud-native AI/ML solutions (AWS/Azure/Google Cloud Platform) and production deployments
• Strong MLOps experience: model lifecycle, governance, monitoring, and production controls.
• Deep GenAI/LLM architecture experience (RAG patterns, evaluation harnesses, prompt orchestration, guardrails)
• Graph/Network analytics exposure (Neo4j/TigerGraph/NetworkX/GraphX).
• Spark/Scala/PySpark at enterprise scale (data processing + ML pipelines)
• BFSI domain exposure and ability to design within regulated / compliance-heavy environments.
Roles & Responsibilities
- Architect and drive the design, development, and deployment of scalable ML/AI solutions.
- Lead and actively contribute to data science initiatives across the full project lifecycle – from
ideation and solution design to hands-on development, deployment, and production support.
- Define standards, best practices, and governance for AI/ML solutioning and model management.
- Collaborate with data engineering, MLOps, product, and business teams.
- Oversee integration of data science models into production systems.
- Design and implement AI solutions on cloud platforms (AWS/Azure/Google Cloud Platform) and/or on-prem using
open-source technologies
- Evaluate and recommend ML tools, frameworks, and cloud-native services aligned to performance,
security, and cost goals.
- Ensure architectures address enterprise non-functionals: scalability, resiliency, observability, security,
and compliance.
- Perform MLOps design and implementation (and lead the team on same if needed) including CI/CD for ML,
reproducibility, model registry, monitoring, drift detection, and operational controls
- Define standards, best practices, and governance for AI/ML solutioning and model management
(validation, documentation, approvals, audits).
- Architect solutions leveraging LLMs (including GPT-class models) for enterprise GenAI use cases and design
patterns such as RAG, guardrails, evaluation and safety controls.
- Guide data strategy and feature store design; align data engineering and ML engineering for reliable feature
pipelines.
- Provide architectural direction for big data processing using Spark/PySpark for large-scale feature generation
and training workflows.
- Partner with stakeholders to identify high-value use cases, shape roadmaps, and translate business requirements
into robust technical architecture
- Present architecture, trade-offs, risk controls, and delivery plans clearly to technical and executive audiences.
- Mentor ML engineers, data scientists, and platform teams; establish architectural guardrails, design reviews,
and engineering standards.
- Open-source contributions or published research.
Generic Managerial Skills, If any
A senior AI/ML Architect (15+ years) to perform end-to-end architecture and delivery of scalable, secure,
production-grade AI/ML systems for BFSI clients. You will own solution blueprints across the full lifecycle—from
use-case discovery and architecture to platform selection, MLOps/LLMOps design, deployment, and governance—
and mentor cross-functional teams (data scientists, ML engineers, data engineers, and application teams). You will work
closely with stakeholders to identify high-value use cases and ensure seamless integration of models into
business applications. Your deep expertise in machine learning, cloud-native architectures, MLOps practices, and
financial domain knowledge will be essential to influence strategy and deliver transformative business impact
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