Senior AI / Platform Engineer

We are seeking a talented Backend Software Engineer with a focus on AI/ML to join our team. In this role, you will be responsible for designing, implementing, and maintaining scalable backend services primarily in Python. Your main tasks will involve building and managing data pipelines for AI/ML applications, as well as operationalizing and deploying Generative AI systems for production use. Design, implement, and maintain scalable backend services in Python Build and maintain data pipelines for AI/ML applications for production use-cases Operationalize and deploy Generative AI systems (APIs, SDKs, pipelines) Develop APIs to serve AI features to downstream applications Orchestrate agentic workflows using frameworks like LangChain, LangGraph, or custom solutions Ensure monitoring, logging, and observability of GenAI services Collaborate cross-functionally to ensure system performance, scalability, and reliability Note: This position is ideal for a hands-on software engineer with significant exposure to AI product development, rather than a data science or AI/ML research role. We are looking for individuals who excel in operational excellence, have deployment experience, and possess systems-level thinking for real AI applications. Requirements Offer Requirements: 5+ years of practical experience as a Python backend developer Strong knowledge of software engineering principles, clean code practices, and testing methodologies Practical experience with Python backend frameworks: FastAPI (preferred), Flask or Django Practical experience with SQL databases and related Python libraries Hands-on experience with cloud platforms: Azure (preferred), AWS or GCP Experience designing and implementing scalable backend services (APIs, data pipelines, SDKs, etc.) Practical experience with building and deploying AI-based applications, including LLMs and embedding models (e.g., OpenAI, Anthropic, Hugging Face) Familiarity with Retrieval-Augmented Generation (RAG) based systems, agentic workflows and Generative AI applications Familiarity with containerized development with Docker Experience with data science Python libraries such as pandas, NumPy, Scikit-learn Experience with Python GenAI frameworks (e.g., LangChain, LangGraph) is a plus Familiarity with CI/CD workflows in GitLab is a plus Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana) is a plus Familiarity with gRPC for high-performance service communication is a plus Familiarity with Model Context Protocol (MCP) is a plus Familiarity with AI observability and tracing tools (e.g., Langfuse) is a plus Originally posted on Himalayas