AI/ML Architect

AI Architect/Engineer Fulltime Bengaluru Key Responsibilities Design, develop, and deploy AI/ML solutions that solve complex business problems. Build and optimize supervised and unsupervised machine learning models for classification, prediction, clustering, anomaly detection, recommendation systems, and pattern recognition. Evaluate business use cases and determine the most suitable algorithms, models, and architectures based on data characteristics and desired outcomes. Architect and implement multi-agent AI systems using modern orchestration frameworks. Develop intelligent workflows using frameworks such as LangGraph, LangChain, Microsoft Agent Framework, and related ecosystems. Build enterprise-grade natural language query systems such as: NL2SQL NL2Cypher NL2GraphQL Design and integrate data platforms leveraging: Relational databases Columnar databases Document stores Graph databases Vector databases (preferred) Establish observability, monitoring, tracing, and debugging capabilities for AI/agent systems. Ensure AI systems are secure, scalable, resilient, and production-ready. Implement governance and compliance controls within agent systems, including access policies, guardrails, auditability, and regulatory alignment. Apply strong software engineering principles, design patterns, and architectural best practices to AI solutions. Collaborate with cross-functional teams including product, engineering, data, and business stakeholders. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field. 8+ years of experience in software engineering, data science, machine learning, or AI engineering roles. Strong expertise in Machine Learning fundamentals, including: Supervised Learning Unsupervised Learning Feature Engineering Model Evaluation Hyperparameter Tuning Drift Detection / Model Monitoring Deep understanding of when to use which ML algorithm based on problem type, dataset quality, scale, and explainability needs. Hands-on experience with Python and ML libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Experience with modern AI orchestration frameworks such as: LangGraph Microsoft Agent Framework Similar agentic AI platforms Strong knowledge of data storage technologies: SQL / Relational databases NoSQL / Document stores Columnar warehouses Graph databases Experience designing secure and scalable distributed systems. Solid understanding of software design patterns and enterprise architecture patterns.