Automation Engineer

Responsibilities - Ideate, design, and drive end-to-end innovation projects, from concept and prototyping to implementation, testing, deployment, and iteration. - Build scalable, cutting-edge solutions leveraging full-stack development, AI, Generative AI (GenAI), and data science to solve complex business problems and create new product capabilities. - Apply AI/GenAI techniques (e.g., LLMs, RAG, prompt engineering, predictive modeling) to develop intelligent features, prototypes, and proof-of-concepts that push product boundaries. - Collaborate cross-functionally with engineering, product, design, and data teams to identify innovation opportunities, assess feasibility, mitigate risks, and deliver high-impact technical solutions. - Conduct technical reviews of systems and emerging technologies to evaluate readiness, identify gaps, risks, and opportunities; provide actionable recommendations and mitigation strategies. - Demonstrate deep understanding of technology stacks from a functional and architectural standpoint; debug complex issues and optimize performance. - Develop and maintain comprehensive documentation for prototypes, AI models, frameworks, and innovative processes. - Mentor junior engineers, lead knowledge sharing, and contribute to Business Intelligence processes (e.g., dashboards, metrics, knowledge base). - Take ownership of personal technical growth, staying current with emerging tools, AI advancements, and best practices in innovation and data science. - Manage multiple priorities effectively, delivering high-quality results in a fast-paced environment. ### Required Skills & Qualifications - 3+ years of hands-on experience in software development and driving technical innovation projects. - Strong full-stack development proficiency (e.g., frontend: React/JavaScript/TypeScript; backend: Node.js/Python/Java/C#; databases: SQL/NoSQL). - Solid programming fundamentals, including data structures, algorithms, and problem-solving. - Proven ability to lead end-to-end innovation projects (from ideation and architecture to delivery and measurement of impact). - Hands-on experience with AI/GenAI (e.g., LLMs, prompt engineering, RAG, fine-tuning) and data science (e.g., Python, pandas, scikit-learn, TensorFlow/PyTorch). - Expertise in modern tools and frameworks (e.g., cloud platforms, CI/CD, prototyping tools, ML pipelines). - Familiarity with cloud platforms (AWS/Azure/GCP), DevOps (Jenkins/GitHub Actions/Docker/Kubernetes), and version control (Git). - Excellent critical thinking, analytical skills, and ability to debug complex, distributed systems. - Strong communication skills with experience presenting technical recommendations and innovative ideas to stakeholders. - Bachelor's/Master's degree in Computer Science, Engineering, or related field (or equivalent experience). ### Preferred Qualifications - Experience building innovative AI-driven features or products (e.g., generative tools, intelligent agents, predictive systems). - Contributions to open-source projects, research, or relevant publications in AI/innovation. - Certifications in cloud, AI/ML, or related fields (e.g., AWS/Azure ML).