Techolution · India
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.
Title: Associate Python AI Engineer
Location: Remote
Shift Timing: 2-11PM
Job Type: Full-Time
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
Non-Negotiable Skills:
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.
