Enterprise AI & ML Architect Role Overview Lead the design of enterprise-grade AI/ML systems with a strong focus on model training, evaluation, and scalable deployment. Provide technical leadership in building AI platforms that ensure training data readiness, robust data pipelines, and production-grade AI reliability, governance, and operational excellence. Key Responsibilities 1. AI/ML Architecture Define Feature–Training–Inference pipelines including data ingestion, data product architecture, model training, evaluation, deployment, and production monitoring. Recommend optimal strategies (Classification, Regression, Clustering, Anomaly Detection, Fine-tuning, RAG, PEFT, or hybrid patterns). Select appropriate statistical, foundation, embedding, and multimodal models aligned to business needs and data structures. Enable MLOps and DataOps using CI/CD, model registry, and data lifecycle management. 2. AI Data Readiness Apply established principles to assess data for quality, bias, coverage, structure, freshness, and sufficiency. Define strategies for data acquisition, data annotation and labelling frameworks, and synthetic data generation and distributed processing (e.g., Spark/PySpark). Establish dataset versioning, enterprise metadata standards (Cataloging, Lineage, Discovery), and governance standards. 3. Model Training,



Evaluation Define training and validation strategies for domain adaptation and production use. Establish robust evaluation frameworks covering statistical performance metrics, offline benchmarking, human evaluation, and safety testing. Implement monitoring, observability, and drift management practices. 4. Governance & Collaboration Embed Responsible AI principles (fairness, explainability, compliance). Partner with Data Architects, AI Engineers, and business stakeholders to deliver production-grade AI systems. Required Experience Overall 15+ years of experience with a foundational background in Data Architecture, 8+ years in AI/ML systems, and 3+ years with LLM/SLM. Proficiency in Data Architecture and Data Modeling principles. Proven experience leading teams in building AI Readiness and large-scale Data Annotation/Labeling solutions. Experience designing scalable, production-grade systems with mature DataOps, MLOps, and AIOps practices. Solid understanding of Classic ML techniques and large-scale model training approaches including fine-tuning and PEFT. Deep experience in building high-scale pipelines using Cloud-based ETL/ELT ecosystems, modern Lakehouse platforms (Databricks, Snowflake, Fabric etc.), and distributed processing (Spark). Experience with hyperscaler AI ecosystems (Azure ML/Foundry, SageMaker, Vertex AI).

📌 Ent AI/ML Architect - SM & AD Level (India)
🏢 People First Consultants
📍 India