Machine Learning Engineer

We expect you to have a Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a similar discipline. You should possess at least 5 years of practical experience in applied AI, NLP, or ML engineering, with a minimum of 2 years focusing directly on LLMs, RAG, semantic search, and Agentic AI. A deep understanding of LLMs (for example, OpenAI, Claude, Gemini), prompt engineering, and their responsible deployment in production settings is essential. Your experience should include the design, construction, and enhancement of RAG pipelines, semantic search, vector databases (like ElasticSearch or Pinecone), and Agentic or multi-agent AI workflows in large-scale production environments. Familiarity with MCP and A2A protocols is a plus, as is exposure to GraphRAG or graph-based knowledge retrieval techniques. Strong proficiency with modern ML frameworks and libraries (such as LangChain, LlamaIndex, PyTorch, and HuggingFace Transformers) is required. You should have the capability to design APIs and scalable backend services, with practical experience in Python. Experience in building, deploying, and monitoring AI/ML workloads in cloud environments (such as AWS or Azure), including services like AWS SageMaker, AWS Bedrock, and AzureAI, is valuable. Additionally, familiarity with tools for load balancing different LLM providers is advantageous. You should also have knowledge of MLOps practices, CI/CD for AI, model monitoring, data versioning, and continuous integration. We’re looking for demonstrated ability to work with large, complex datasets, perform data cleaning and feature engineering, and develop scalable data pipelines.