Principal ML Architect (Immediate joiner only)

Job Description

Job Title: Principal Machine Learning Architect Location: Bengaluru, Hybrid Department: Engineering / Data Science Reports To: Head of AI/ML About the Role: We are seeking a highly experienced Principal Machine Learning Architect to lead the design, development, and deployment of large-scale, high-impact ML solutions across our organization. As a thought leader, you will drive the ML roadmap, architect end-to-end solutions, and ensure our ML infrastructure is scalable, secure, and production-ready. Key Responsibilities: Architecture Leadership: Design and oversee the implementation of scalable, reliable, and efficient machine learning architectures. End-to-End Ownership: Lead the entire ML lifecycle from problem definition, data ingestion, feature engineering, model development, deployment, and monitoring. Strategic Guidance: Define best practices for ML system design, governance, LLM, NLP, Deep Learning, Machine Learning, RAG, MLOps, and responsible AI practices. Collaboration: Work cross-functionally with data scientists, ML engineers, product managers, and infrastructure teams to ensure alignment and integration. Innovation: Evaluate and incorporate new tools, techniques, and research to improve model performance and infrastructure. Mentorship: Provide technical mentorship to ML engineers and data scientists; promote a culture of technical excellence and continuous learning. Scalability: Architect and optimize ML solutions for scalability, performance, and maintainability in cloud and hybrid environments. Security & Compliance: Ensure models and data pipelines adhere to security, fairness, and compliance standards. Required Qualifications: 10+ years of experience in software engineering or data science with at least 5 years in ML architecture or applied ML roles. Advanced degree (MS/PhD) in Computer Science, Machine Learning, Data Science, or a related field. Proven experience building and deploying ML systems at scale (e.g., recommendation systems, NLP, computer vision, time series). Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Deep understanding of data infrastructure, distributed computing, and modern MLOps practices (e.g., MLflow, Kubeflow, Airflow). Familiarity with cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes). Strong knowledge of data privacy, model governance, and responsible AI principles. Preferred Qualifications: Experience with generative AI or LLM deployment. Contributions to open-source ML projects or research publications. Familiarity with data mesh, feature stores, or real-time ML pipelines. Strong communication and leadership skills.Role & responsibilities Preferred candidate profile