Machine Learning Architect - Artificial Intelligence

Job Description 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:

1. Regression

2. Classification

3. Clustering

4. Decision Trees

5. Random Forest

6. Gradient Boosting (XGBoost, LightGBM, CatBoost)

7. Support Vector Machines (SVM)

8. Time Series Forecasting

9. Recommendation Systems

10. 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.

- Strong 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.

(ref:hirist.tech)