Overview

We are seeking an experienced AI/ML Architect with strong expertise in machine learning system design, production model deployment, and hands-on coding ability.

Responsibilities

• We are seeking an experienced AI/ML Architect with strong expertise in machine learning system design, production model deployment, and hands-on coding ability. The ideal candidate will be highly skilled in architecting end-to-end ML solutions, conducting code reviews across ML pipelines, and delivering scalable predictive analytics systems for enterprise clients. This is a hands-on architecture role requiring both technical depth and delivery leadership.
• Design, develop, and maintain end-to-end ML architectures covering data ingestion, feature engineering, model training, deployment, and monitoring.
• Perform code reviews across ML pipelines, model implementations, and deployment scripts to maintain engineering quality standards.
• Build and deploy production machine learning models using frameworks such as LightGBM, XGBoost, scikit-learn, PyTorch, and TensorFlow.
• Implement MLOps practices including model versioning, monitoring, retraining pipelines, and drift detection.
• Optimize model performance, diagnose data quality issues, and resolve production model degradation.
• Translate ambiguous client problem statements into technically sound, deliverable ML solutions.
• Lead and mentor a team of ML engineers and data scientists, establishing coding and validation standards.
• Act as technical authority in client discussions, solution workshops, and pre-sales engagements.
• Scope and estimate new ML opportunities and support proposals and RFP responses.
• Working under a dynamic agile based environment.
• Strictly adhere to scrum framework and guidelines.
• Coordinate with multiple development teams.

Qualifications

• Overall experience: 10+ Years
• 6+ years of professional experience in machine learning, data science, or applied AI, with 3+ years in an architect or technical lead capacity.
• Strong hands-on coding proficiency in Python with production ML framework experience.
• Demonstrated experience taking models from prototype to production, not limited to POC or research work.
• Proven ability to conduct code reviews across ML pipelines, data engineering, and model deployment.
• Hands-on experience with classical ML techniques including supervised learning, anomaly detection, time-series analysis, and imbalanced classification.
• Working knowledge of MLOps tooling and production model lifecycle management.
• Cloud platform experience (Azure, AWS, or GCP) including ML services and deployment infrastructure.
• Proficiency with SQL and large-scale data processing.
• Familiarity with version control systems (Git, SVN, etc.).
• Strong problem-solving skills and ability to work independently.
• Exposure to log analytics, observability data, or large-scale unstructured data processing.
• Knowledge of REST API integration for model serving and inference endpoints.
• Client-facing consulting or services delivery background.
• Advanced degree (MS or PhD) in Computer Science, Statistics, Applied Mathematics, or related field.