Lead Agentic AI Engineer

Department/Project Description

Our client is the leading TV streaming platform in the US and Canada used by advertisers to manage their online campaigns across all media channels, device platforms, advertising exchange, manipulate data, and manage client's services. The project aims to implement various applications and services for Data Engineering and Advertising systems allowing advertisers to manage their entire campaigns - including OTT, linear TV, omnichannel, and more - all in one place.

Job Description

• Bachelor’s or master’s degree in computer science, Computer Engineering, Machine Learning, or a related field, with 5+ years of overall industry experience;
• Strong computer science fundamentals; able to design and implement efficient algorithms with ease;
• 1+ years of hands-on experience with agentic AI and/or LLM-based agents or applications;
• Proven experience designing and building middleware platforms, REST APIs, and distributed systems at scale;
• Advanced proficiency JavaScript and/or Python, with deep familiarity with AI/ML libraries (PyTorch, Transformers, LangChain, etc.);
• Solid understanding of microservices architecture, message queues (Kafka, Pub/Sub etc), and cloud-native development (AWS/GCP);
• Experience working with vector databases and retrieval-augmented generation (RAG) pipelines;
• Demonstrated success building and deploying agentic AI use cases in production, such as sales automation, operations agents, or analytics bots;
 

Job Responsibilities

• Build intelligent agents that plan, reason, and take actions using LLMs (GPT, Gemini, Claude, LLaMA, etc.);
• Implement RAG pipelines, long-term memory, action orchestration, and multi-agent collaboration;
• Translate business requirements into autonomous workflows and smart features that generate business value;
• Apply advanced prompt engineering, tool use, and function-calling strategies to drive agent behavior;
• Design and implement robust middleware that connects APIs, AI models, databases, and services in scalable ways;
• Architect service-oriented and event-driven systems that enable real-time decision-making by agents;
• Build secure, resilient integration layers that facilitate communication between business systems (CRM, ERP, OMS, etc.);
• Ensure observability, monitoring, and governance of agent operations and system interactions;
• Partner with product managers, data engineers, and backend teams to align system capabilities with business goals;
• Lead architectural discussions and make trade-off decisions regarding scalability, modularity, and performance;
• Mentor engineers and contribute to architectural standards and AI enablement framework;