Senior AI Engineer

As a highly experienced Senior AI Engineer, your role will involve designing intelligent, autonomous agents capable of decision-making, tool usage, and multi-step reasoning in enterprise environments. You will be expected to design and develop Agentic AI solutions, build and orchestrate workflows using LangChain and LangGraph, and implement multi-agent systems for complex problem-solving scenarios. Additionally, you will enable tool calling/function calling capabilities for AI agents, integrate AI agents with enterprise APIs, databases, and external systems, and design scalable and fault-tolerant AI architectures for production environments. It is essential to work closely with cross-functional teams (DevOps, Data Engineering, Product) to optimize performance, latency, and cost of AI systems. Key Responsibilities: - Design and develop Agentic AI solutions using Agentic AI principles - Build and orchestrate workflows using LangChain and LangGraph - Implement multi-agent systems for complex problem-solving scenarios - Enable tool calling / function calling capabilities for AI agents - Integrate AI agents with enterprise APIs, databases, and external systems - Design scalable and fault-tolerant AI architectures for production environments - Work closely with cross-functional teams (DevOps, Data Engineering, Product) - Optimize performance, latency, and cost of AI systems Qualifications Required: - 8+ years in software engineering with strong Python expertise - Hands-on experience with LangChain and/or LangGraph - Strong understanding of LLM architectures and APIs (OpenAI, etc.) - Experience in building autonomous agents and workflow orchestration - Knowledge of Multi-Agent Systems - Exposure to cloud platforms (AWS/Azure/GCP) - Experience with containerization and orchestration (Docker, Kubernetes) - Strong debugging and problem-solving skills Additional Details: - Good to Have: - Experience with Retrieval-Augmented Generation - Knowledge of MLOps / LLMOps - Familiarity with AI safety and AI Guardrails - Exposure to real-time or streaming AI applications Key Competencies: - Ownership mindset and ability to lead AI initiatives end-to-end - Strong communication and stakeholder management skills - Ability to work in fast-paced, innovation-driven environments As a highly experienced Senior AI Engineer, your role will involve designing intelligent, autonomous agents capable of decision-making, tool usage, and multi-step reasoning in enterprise environments. You will be expected to design and develop Agentic AI solutions, build and orchestrate workflows using LangChain and LangGraph, and implement multi-agent systems for complex problem-solving scenarios. Additionally, you will enable tool calling/function calling capabilities for AI agents, integrate AI agents with enterprise APIs, databases, and external systems, and design scalable and fault-tolerant AI architectures for production environments. It is essential to work closely with cross-functional teams (DevOps, Data Engineering, Product) to optimize performance, latency, and cost of AI systems. Key Responsibilities: - Design and develop Agentic AI solutions using Agentic AI principles - Build and orchestrate workflows using LangChain and LangGraph - Implement multi-agent systems for complex problem-solving scenarios - Enable tool calling / function calling capabilities for AI agents - Integrate AI agents with enterprise APIs, databases, and external systems - Design scalable and fault-tolerant AI architectures for production environments - Work closely with cross-functional teams (DevOps, Data Engineering, Product) - Optimize performance, latency, and cost of AI systems Qualifications Required: - 8+ years in software engineering with strong Python expertise - Hands-on experience with LangChain and/or LangGraph - Strong understanding of LLM architectures and APIs (OpenAI, etc.) - Experience in building autonomous agents and workflow orchestration - Knowledge of Multi-Agent Systems - Exposure to cloud platforms (AWS/Azure/GCP) - Experience with containerization and orchestration (Docker, Kubernetes) - Strong debugging and problem-solving skills Additional Details: - Good to Have: - Experience with Retrieval-Augmented Generation - Knowledge of MLOps / LLMOps - Familiarity with AI safety and AI Guardrails - Exposure to real-time or streaming AI applications Key Competencies: - Ownership mindset and ability to lead AI initiatives end-to-end - Strong communication and stakeholder management skills - Ability to work in fast-paced, innovation-driven environments