Associate AI Python Engineer

About the Role

We are looking for an energetic and driven Associate Python AI Engineer to join our engineering team and contribute to the development of scalable, AI-powered applications and backend systems. In this role, you will work closely with senior engineers, AI/ML engineers, product teams, and other stakeholders to build production-ready Python applications, AI/LLM solutions, APIs, data pipelines, and cloud-native services.

Job Type: Full-Time

Location: Remote

Shift Timing: 2-11PM

Key Responsibilities

• Python Application Development: Design, develop, test, and maintain robust and scalable backend applications using Python and industry-standard development practices.
• AI & LLM Application Development: Contribute to the development of LLM-powered applications, including Retrieval-Augmented Generation (RAG), vector search, document processing, and AI-driven workflows.
• Backend & API Development: Build and integrate secure, scalable APIs and backend services using frameworks such as FastAPI, Flask, or Django.
• Database Development: Work with relational and NoSQL databases to design schemas, write efficient queries, manage data, and build reliable data-access layers.
• Data & ETL Engineering: Develop and maintain ETL/data-processing workflows, ensuring reliable ingestion, transformation, validation, and movement of data across systems.
• Asynchronous Processing: Implement background jobs and distributed task processing using technologies such as Celery and Redis.
• DSA & Problem Solving: Apply strong data structures and algorithmic thinking to develop efficient, optimized, and maintainable solutions.
• Cloud & Deployment: Develop and deploy applications on AWS/GCP, leveraging cloud services and following cloud-native engineering practices.
• Containerization: Containerize applications using Docker and contribute to reliable and repeatable deployment processes.
• CI/CD & Engineering Practices: Work with CI/CD pipelines to automate application testing, builds, deployments, and release processes.
• Code Quality & Collaboration: Write clean, modular, testable code; participate in code reviews; troubleshoot production issues; and collaborate with cross-functional engineering teams.
• Continuous Learning: Stay current with emerging developments in Generative AI, LLMs, AI agent frameworks, backend engineering, cloud technologies, and distributed systems.

Requirements & Tech Stack

• Python: Strong understanding of Python fundamentals, OOP, exception handling, modules, packages, and writing clean, maintainable production-quality code.
• Data Structures & Algorithms (DSA): Strong problem-solving ability with a good understanding of arrays, strings, hash maps, stacks, queues, trees, graphs, sorting, searching, and algorithmic complexity.
• SQL: Strong understanding of SQL, including joins, subqueries, aggregations, indexing, transactions, and query optimization.
• AlloyDB & Firestore: Understanding of relational and NoSQL database concepts with hands-on experience in AlloyDB and/or Firestore preferred.
• FastAPI / Flask / Django: Experience building REST APIs and backend services using at least one modern Python web framework.
• LLM & RAG: Understanding of Large Language Models, RAG architecture, embeddings, document processing, and vector search.
• Celery & Redis: Understanding of asynchronous/background task processing, caching, queues, and distributed application patterns using Celery and Redis.
• ETL Design: Understanding of data ingestion, transformation, validation, processing, and pipeline design.
• Docker: Ability to containerize Python applications and understand fundamental Docker concepts such as images, containers, networking, and volumes.
• CI/CD: Understanding of continuous integration and continuous deployment concepts, automated testing, build pipelines, and deployment workflows.
• Cloud – AWS/GCP: Familiarity with cloud computing concepts and hands-on exposure to deploying or operating applications on AWS or GCP.
• Git: Strong understanding of Git, including branching, merging, pull requests, conflict resolution, and collaborative development workflows.
• AI Agents: Exposure to AI agents, tool calling, agent orchestration, memory, and multi-agent workflows.
• Vector Databases: Hands-on experience with vector databases or vector search technologies such as Pinecone, Weaviate, Qdrant, Milvus, or equivalent.
• Cloud AI Services: Exposure to AI/ML services offered by AWS or GCP is an added advantage.

Negotiable Skills

• LangChain: Experience building LLM-powered applications and workflows using LangChain.
• LangGraph: Understanding of graph-based AI workflows, stateful agent orchestration, and multi-step LLM applications using LangGraph.
• Kubernetes: Basic understanding of Kubernetes concepts including pods, deployments, services, configuration, and container orchestration.