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
Requirements
You’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
Skills: machine learning,embeddings,ml,cloud-native environments,semantic search,nlp,pytorch,data engineering for ml,generative ai,artificial intelligence,prompt engineering,tensorflow,python,hugging face transformers,large language models,aws,mlops practices,open source,open ai,vector databases
