Senior AI Engineer – Agentic Systems

About the role

We are seeking a highly skilled Senior AI Engineer with 5 to 7 years of professional experience to design, develop, and deploy autonomous AI agents and multi-agent systems. In this role, you will build intelligent workflows that reason, plan, execute complex tasks, and seamlessly interact with enterprise applications. You will leverage Large Language Models (LLMs), cutting-edge agent orchestration frameworks, and cloud AI platforms to automate business processes and drive operational efficiency.

Key responsibilities

• Design and develop production-grade Agentic AI solutions utilizing advanced LLMs and agent frameworks
• Build autonomous agents capable of reasoning, planning, tool execution, and multi-step workflows, as well as complex multi-agent orchestrated systems
• Integrate AI agents with enterprise systems, APIs, internal databases, and modern cloud services to power business automation
• Implement robust memory systems, dynamic context management, RAG (Retrieval-Augmented Generation), and knowledge-grounding capabilities
• Apply Responsible AI principles, rigorous governance, data security, and compliance controls throughout the entire solution lifecycle
• Conduct ongoing testing, evaluation, monitoring, and performance optimization of AI agents in live production environments
• Partner directly with business stakeholders to identify high-impact Agentic AI use cases and translate them into functional requirements
• Author detailed technical documentation, build reusable frameworks, and establish engineering best practices for the AI development team

About you

• 5 to 7 years of professional experience
• Strong technical expertise in Agentic AI frameworks (e.g., LangChain, CrewAI, AutoGen, or LangGraph) paired with advanced Python development skills
• Exceptional verbal and written communication skills to articulate complex AI concepts to both technical teammates and business stakeholders
• Proven experience working with foundational models (OpenAI, Anthropic, open-source models) for reasoning, function calling, and tool usage
• Solid foundation in backend engineering, API integration, and handling structured/unstructured data
• Experience with vector databases (e.g., Pinecone, Chroma, Milvus) and optimization of RAG pipelines
• Familiarity with enterprise cloud platforms (AWS, Azure, or GCP) and AI orchestration deployment
• Knowledge of MLOps / LLMOps practices for monitoring drift, latency, and costs of agentic workflows