AI Engineer

Technical Skills 1.   Strong proficiency in Python + ML ecosystem (PyTorch, TensorFlow, scikit‑learn,   Hugging Face, LangChain). 2.   Proven experience deploying production-grade AI/ML systems, as reflected in   organizational resumes (e.g., computer vision pipelines, real-time inference   services). 3.   Hands-on experience with LLMs, RAG architecture, vector databases (FAISS,   Qdrant, Azure AI Search). 4.   Solid understanding of software engineering fundamentals: API development,   microservices, distributed systems. 5.   Experience with Docker and Kubernetes, widely used in production deployments   across internal candidates. 6.   Proficiency with cloud platforms (GCP preferred; AWS/Azure also welcome). 7.   Strong grasp of CI/CD, automated testing, model lifecycle management, and   observability. 8.   Familiarity with real-time processing (streaming, queues, event-driven   architecture). Soft   Skills 1. Fast   learner with ability to adopt new AI frameworks and technologies quickly. 2.   Strong problem-solving mindset with practical approach to converting POC into   production systems. 3.   Clear communicator who can explain technical concepts to a variety of   stakeholders.