AI Researcher

Experiences Total Experience: 812 years DevOps / Software Engineering Background: 5–8 years of solid hands-on development and system integration experience AI-Specific Experience: 3–4 years minimum working directly with LLMs, AI automation, or agentic systems Role Description 1. AI Research & Innovation Conduct research on Agentic AI architectures and multi-agent orchestration for developer workflow automation. Explore and implement AI-driven systems for automatic code review, SonarQube integration, and report analysis. Research and apply prompt engineering techniques for domain-specific code generation and analysis tasks. Evaluate multi-model AI systems (ChatGPT, Codex, Claude, Cursor AI etc.) for hybrid workflow optimization. 2. Design , Development & Integration Architect and develop AI-enabled DevOps pipelines integrating models like Codex into GitLab/Jenkins CI/CD workflows. Build agentic frameworks for autonomous code Review, static analysis, and code correction loops. Integrate AI into developer tools such as GitLab, Jenkins, SonarQube, and other CI/CD platforms. 3. Evangelism & Knowledge Leadership Serve as an AI thought leader and internal evangelist, driving adoption of AI-powered engineering workflows. Conduct workshops, POCs, and internal tech talks on Agentic AI, prompt engineering, and AI-assisted development. Mentor engineers in leveraging AI responsibly to enhance productivity, security, and code quality. Required Skills & Qualifications: Programming: Strong coding skills with experience in Python. Familiarity with C/C++, Java, ReactJS, Node.js is a plus. DevOps & CI/CD: Experience with GitLab CI/CD, Jenkins, Docker, Kubernetes, and pipeline orchestration. AI Tools: Hands-on with ChatGPT, Codex, Claude AI, Cursor AI etc. Agentic AI Systems: Demonstrated experience building or researching autonomous multi agent systems. Prompt Engineering: Certified or verifiable experience in prompt design, optimization, and evaluation. Version Control & Workflow: Advanced Git knowledge (branching, merging, hooks, and PR automation). Strong understanding of LLM model behavior, tokenization, and reasoning patterns. Experience designing or tuning AI-assisted code review frameworks. Deep knowledge of software design patterns, architecture principles, and code quality metrics. Experience integrating AI-based decision support systems in CI/CD pipelines. Evangelist mindset — able to inspire, educate, and lead AI adoption across technical teams. Strong analytical and problem-solving ability; able to bridge research and production. Excellent communication and technical writing skills. Adaptable and curious — passionate about staying ahead of emerging AI trend Educational Qualifications: Bachelor’s degree in Computer science, Artificial Intelligence, or Software Engineering. Research publications or open-source contributions in agentic AI systems, AI code assistance, or DevOps-AI integration. #J-18808-Ljbffr