AI Engineer

AI Agent Development: Design, develop, and implement autonomous AI agents using LLMs, Lyzr agent frameworks, and custom orchestration logic to solve complex enterprise workflows.

Data Pipeline Design: Build robust data pipelines for data ingestion, preprocessing, feature engineering, embedding generation, and vector database management to support AI agent operations.

Agent Orchestration & Workflow Automation: Design multi-agent systems with complex workflows, conditional logic, human-in-the-loop controls, and integration with enterprise systems (CRM, ERP, databases, APIs).

Production Deployment: Deploy AI models and agents to production environments (cloud, on-premise) using containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines. Ensure scalability and reliability.

Research & Innovation: Stay current with latest AI research, LLM advancements, agent frameworks, and emerging techniques. Prototype and evaluate new approaches to improve agent capabilities.

Cross-Functional Collaboration: Work with solutions architects, product managers, and engineers to define requirements, align on technical approaches, and deliver integrated solutions.

Code Quality & Documentation: Write clean, maintainable, and well-documented code. Conduct code reviews, create technical specifications, and maintain comprehensive documentation for AI systems.

Testing & Validation: Implement rigorous testing frameworks for AI models and agents including unit tests, integration tests, agent simulation, adversarial testing, and performance benchmarking.

1. Security & Compliance: Implement security best practices including data encryption, PII redaction, access controls, and compliance with enterprise security requirements (SOC2, GDPR, HIPAA).