Experience Years: 5 - 15 Years Must Have: 3+ years in building and deploying machine learning systems in production Deep expertise in Python and modern AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers) Experience with large language models (OpenAI, Anthropic, Cohere, open source LLMs) and prompt engineering What You’ll Do Architect and implement advanced AI and machine learning systems that solve complex business problems Lead the design and deployment of LLM-based applications using frameworks like LangChain, LlamaIndex, and vector databases Develop end-to-end ML pipelines from data acquisition and model training to deployment and monitoring Design and build AI copilots, agents, and generative workflows that integrate seamlessly into modern software ecosystems Apply deep expertise in NLP, computer vision, or predictive modeling to build intelligent, real-time systems Evaluate and fine-tune foundation models for custom enterprise use cases Collaborate with cross-functional product, design, and engineering teams to define intelligent experiences Explore and implement retrieval-augmented generation (RAG), semantic search, and multi-modal reasoning techniques Contribute to internal AI frameworks, toolkits, and accelerators to speed up solution delivery Mentor engineers on AI architecture, model lifecycle best practices, and ethical/secure use of machine learning RequirementsYou’ll bring: 5+ years of software engineering experience with a strong focus on AI/ML and intelligent systems 3+ years in a technical leadership role, building and deploying machine learning systems in production Deep expertise in Python and modern AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers) Experience with large language models (OpenAI, Anthropic, Cohere, open source LLMs) and prompt engineering Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and scalable ML infrastructure Knowledge of AI system design, data engineering for ML, model evaluation, and MLOps practices Strong understanding of NLP, generative AI, embeddings, and semantic search Experience integrating AI capabilities into full-stack applications and cloud-native environments, specifically within AWS Strong communication skills and a consulting mindset—able to confidently lead client-facing discussions on AI strategy Passion for experimentation, innovation, and shaping the future of applied AI