AI Research Engineer - Agentic AI & Intelligent Automation

Key Responsibilities

• Design Agentic AI Applications: Design and implement agentic AI applications Architect AI agent frameworks capable of autonomously analyzing visual and operational data, making decisions, and coordinating actions (like alerts or diagnostics) within the surveillance ecosystem.
• Design RAG Applications: Build Retrieval-Augmented Generation (RAG) systems that combine large language models with structured knowledge bases (logs, system data, documentation) to enable context-aware analytics, troubleshooting, and operational insights.
• Intelligent Robotic Process Automation (RPA) : Design AI based software tools to automate repetitive or rule-based digital tasks normally performed by humans to reduce manual workload and improve system efficiency. Develop and implement intelligent automation pipelines to streamline surveillance operations, such as video data handling and device monitoring by integrating RPA with AI models.
• QA Automation: Automate QA based applications and integrate AI into QA automation. Design and maintain automated testing frameworks for device software to ensure consistent performance, reliability, and accuracy across camera analytics, detection modules, and cloud-based services.
• Research on Cutting Edge Technologies: Continuously explore advancements in AI, computer vision, edge computing, and automation to identify innovative solutions that enhance the intelligence and adaptability of surveillance products.
• Deployment and Integration: Manage the end-to-end deployment, scaling, and integration of AI and automation modules across on-premise devices and cloud environments, ensuring smooth interoperability within the company’s surveillance infrastructure.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Artificial Intelligence, or a related technical field.
• Strong programming skills in Python.
• Proven experience with modern deep learning frameworks such as PyTorch (preferred) or TensorFlow.
• Hands-on experience in working with Agentic AI systems, Multi-Agent workflows (LangGraph or other Agentic AI system design frameworks).
• Solid understanding of LLMs and experience in developing Retrieval-Augmented Generation (RAG) applications or LLM-based conversational systems (using Vector Databases and frameworks such as LangChain or similar RAG development tools).
• Prior contributions to research papers or projects.

Preferable Skills and Experience

• (Preferred) Prior research contributions, such as technical papers, open-source projects, or academic collaborations.
• Familiarity with MLOps principles and tools (e.g., Docker, MLflow) for managing the machine learning lifecycle.
• Familiarity with Robotic Process Automation (RPA) tools (UiPath, Power Automate, PyAutoGUI).
• Familiarity with QA automation frameworks (e.g., Selenium, Playwright) is a plus.
• A portfolio of relevant projects, a GitHub profile showcasing your work, or contributions to open-source projects.

Soft Skills:

• Strong problem-solving abilities and attention to detail.
• Excellent communication skills for documentation and collaboration.
• Ability to work effectively in a team-oriented environment.