AL/ML Technical Architect

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/GCP) 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/GCP) 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

Salary Range: $180,000 to $210,000 per year