Associate Architect Machine learning

Role & Responsibilities:

Design and architect multi-layered ML solutions using Azure AI Foundry and ensuring they are robust, scalable, and production-ready.

Defining and implementing robust integration strategies between legacy control software (e.g., VB6) and modern Azure ML platforms, ensuring seamless data flow, scalability, and strict data security governance.

Architecting, developing, and deploying advanced machine learning and deep learning models specifically for curve prediction and automated quality scoring.

Designing and implementing sophisticated optimization engines to automate precision movements with micron-level accuracy.

Leading the transition from manual operator heuristics and rule-based systems to automated, data-driven logic and decision-making processes.

Performing extensive data pre-processing on historical calibration data to train and validate models, and building robust production monitoring, alerting, and retraining scripts.

Continuously enhancing, modifying, optimizing, and maintaining existing ML models to improve performance, accuracy, and efficiency.

Working closely with external and internal stakeholders to define requirements for ML use cases, gather feedback, and drive solution enhancements.

Generating actionable insights from large datasets and effectively communicating complex analysis results and model performance to clients and internal stakeholders.

Provide technical leadership to MLEs and Platform engineers to ensure architectural alignment across workstreams.

Skills Expectation:

Expertise in Python and SQL with PySpark for large-scale data processing.

Experience Expertise in Tree-based models (e.g., XGBoost, LightGBM).

Strong foundation in Traditional ML algorithms (e.g., Logistic Regression, Cluster Analysis, Decision Trees, Statistical Modeling, Predictive Analysis, Regression, Classification).

Experience with Encoder-Decoder architectures for sequence modeling.

Proficiency in Deep Learning Architectures specifically for time series and curve data.

Deep expertise in Agentic AI, Time Series forecasting, Descriptive Machine Learning, and Exploratory Data Analysis.

Strong experience with Google Cloud Platform (GCP) Or Azure Cloud Services for ML workloads.

Proven ability to design, develop, and deploy production-grade Machine Learning and Generative AI systems that deliver measurable ROI and contribute to long-term AI roadmaps.

Experience with advanced optimization techniques for black-box functions, including Bayesian Optimization and sophisticated hyperparameter tuning methods.

Experience with Databricks for ML workflows and data engineering.

Must have experience in putting models into production.

Experience in post-production deployment, including MLOps practices (monitoring, retraining, versioning), would be a significant plus.

Experience working with scalable, highly-interactive, high-performance ML systems and projects.

Great analytical skills with meticulous attention to detail.

Strong stakeholder and team management skills.