Artificial Intelligence Engineer - LLM/Agentic AI

Cloud & Infrastructure Development Support the development, automation, and maintenance of robust, scalable cloud-based infrastructures for AI system development and data operations.

- Data Analysis & Processing Collect, process, and analyze large datasets from mobile radio testing environments to extract actionable insights.

- Traditional ML & Deep Learning Apply machine learning and deep learning techniques (e.g., classification, regression, feature extraction) to address challenges in mobile radio testing.

- GenAI Applications Explore, implement, and benchmark AI systems using Large Language Models (LLMs) and related concepts such as RAG, Agentic AI workflows, MCP, and evaluate different solutions to enhance product capabilities.

- DevOps / MLOps Integration Help automate DevOps and MLOps workflows, ensuring seamless deployment and integration of AI solutions into products. Work with technologies such as containerization and cloud platforms.

- Product Development Contribute to end-to-end product development, including application and web-UI design.

Your Qualifications :

- Bachelor/Master/PhD in Computer Science, Artificial Intelligence, or related field from a reputed university or college.

- 3+ years of professional experience in software development, including 2+ year of proven experience in AI / ML.

- Mandatory hands-on experience in Agentic AI systems and Core Large Language Model (LLM) development.

- Exposure and experience in LLM engineering, including prompt engineering, RAG architectures, agentic workflows, MCP, and benchmarking practices using tools such as RAGAS.

- Proficiency in Python programming, particularly with libraries for data processing and AI / ML development.

- Strong understanding of AI / ML fundamentals, including architectures such as CNN, Transformers, model training, fine-tuning, and benchmarking.

- Interest or exposure to DevOps / MLOps tools and practices.

- Understanding of containerization (Docker) and experience in cloud engineering (Kubernetes) on platforms such as Microsoft Azure is a plus.

- Experience with web frameworks (e.g., FastAPI, React) is an advantage.

- Ability to work effectively and independently in a diverse, international, and collaborative environment.

- Excellent communication, analytical, debugging, and problem-solving skills.