Machine Learning Engineer

Role Overview: As part of the team, you will be responsible for designing and developing a modular, scalable AI platform to serve foundation models and RAG-based applications. Your role will involve building pipelines for embedding generation, document chunking, and indexing. Additionally, you will develop integrations with vector databases like Pinecone, Weaviate, Chroma, or FAISS, and orchestrate LLM flows using tools like LangChain, LlamaIndex, and OpenAI APIs. Implementing RAG architectures to combine generative models with structured and unstructured knowledge sources will also be a key part of your responsibilities. You will create robust APIs and developer tools for easy adoption of AI models across teams and build observability and monitoring into AI workflows for performance, cost, and output quality. Collaboration with DevOps, Data Engineering, and Product teams to align platform capabilities with business use cases will also be essential. Key Responsibilities: - Design and develop a modular, scalable AI platform for foundation models and RAG-based applications. - Build pipelines for embedding generation, document chunking, and indexing. - Develop integrations with vector databases like Pinecone, Weaviate, Chroma, or FAISS. - Orchestrate LLM flows using tools like LangChain, LlamaIndex, and OpenAI APIs. - Implement RAG architectures to combine generative models with structured and unstructured knowledge sources. - Create robust APIs and developer tools for easy adoption of AI models across teams. - Build observability and monitoring into AI workflows for performance, cost, and output quality. - Collaborate with DevOps, Data Engineering, and Product teams to align platform capabilities with business use cases. Qualifications Required: - Strong experience in Python, with deep familiarity in ML/AI frameworks (PyTorch, Hugging Face, TensorFlow). - Experience building LLM applications, particularly using LangChain, LlamaIndex, and OpenAI or Anthropic APIs. - Practical understanding of vector search, semantic retrieval, and embedding models. - Familiarity with AI platform tools (e.g., MLflow, Kubernetes, Airflow, Prefect, Ray Serve). - Hands-on experience with cloud infrastructure (AWS, GCP, Azure) and containerization (Docker, Kubernetes). - Solid grasp of RAG architecture design, prompt engineering, and model evaluation. - Understanding of MLOps, CI/CD, and data pipelines in production environments.,