Machine Learning Architect:

Experience :

15+ Years

Role Overview :

We are seeking a highly experienced Machine Learning Architect with 15+ years of experience in Data Science, Advanced Analytics, and Artificial Intelligence, including extensive expertise in traditional Machine Learning.

The ideal candidate will be responsible for defining enterprise AI/ML strategy, architecting scalable ML platforms, and leading the design and implementation of advanced analytics solutions.

This role requires deep expertise in statistical modeling, predictive analytics, MLOps, cloud-native ML platforms, and technical leadership.

Exposure to Generative AI is desirable but not the primary focus.

Key Responsibilities :

- Define and drive the enterprise Machine Learning architecture and roadmap aligned with business objectives.
- Architect scalable, secure, and high-performance AI/ML solutions for large-scale enterprise applications.
- Lead the design and implementation of predictive, prescriptive, and advanced analytics solutions.
- Evaluate and recommend the most appropriate ML algorithms, modeling techniques, and architectural patterns.
- Design end-to-end ML pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and retraining.
- Establish MLOps best practices, model governance, explainability, observability, and responsible AI frameworks.
- Partner with business leaders and cross-functional teams to translate business challenges into AI-driven solutions.
- Provide technical leadership and mentor Data Scientists, ML Engineers, and Architects.
- Drive innovation by evaluating emerging AI/ML technologies and incorporating industry best practices.
- Ensure AI solutions meet enterprise standards for scalability, security, compliance, and performance.

Required Skills :

- 15+ years of experience in Data Science, Advanced Analytics, Artificial Intelligence, and Machine Learning, with significant experience in architecture and technical leadership.
- Deep expertise in Python, SQL,



and the scientific Python ecosystem (Pandas, NumPy, Scikit-learn).
- Strong foundation in Statistics, Probability, Linear Algebra, Optimization, Experimental Design, and Predictive Analytics.
- Extensive hands-on experience with traditional Machine Learning techniques, including:
- Regression
- Classification
- Clustering
- Decision Trees
- Random Forest
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Support Vector Machines (SVM)
- Time Series Forecasting
- Recommendation Systems
- Anomaly Detection
- Expertise in feature engineering, model evaluation, explainability (SHAP/LIME), and model optimization.
- Experience designing and implementing enterprise-scale MLOps using MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, or similar platforms.
- Solid experience with distributed data processing frameworks such as Apache Spark.
- Hands-on experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI/ML services.
- Strong understanding of data architecture, data engineering, and modern analytics platforms.
- Experience with data visualization and BI tools such as Power BI or Tableau.
- Excellent client-facing, stakeholder management, solution architecture, and leadership skills.

Good to Have :

- Experience with Deep Learning frameworks such as TensorFlow or PyTorch.
- Exposure to NLP, Computer Vision, and Graph Analytics.
- Familiarity with Generative AI, LLMs, AI Agents, and RAG architectures.
- Experience with Responsible AI, AI Governance, and Model Risk Management.

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or a related discipline.
- Experience leading enterprise AI transformation programs and architecting AI platforms for large organizations.
- Domain expertise in BFSI, Healthcare, Retail, Manufacturing, or Telecommunications is preferred.
- This JD is aligned with a Principal/Enterprise Machine Learning Architect profile, emphasizing analytics, data science, traditional ML, enterprise architecture, MLOps, and executive-level technical leadership rather than a GenAI-centric role.

📌 Machine Learning Architect - Artificial Intelligence (India)
🏢 Sourcebae
📍 India