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.
