If you’re someone who has already started working with Python, explored Generative AI, and you’re excited about building real-world AI applications, this opportunity is absolutely worth your attention.
This Junior AI Engineer role is ideal for early-career professionals who want to move beyond tutorials and actually work on production-grade AI systems with guidance from experienced engineers.
📌 Job Overview (Explained Simply)
As a Junior AI Engineer, you’ll be working closely with senior engineers to design and build Generative AI applications. Don’t worry — this is not a “you must know everything” role. It’s more about having strong basics, curiosity, and the willingness to learn fast.
- 🧑💻 Job Role: Junior AI Engineer
- 🕒 Job Type: Full-time
- 📍 Location: Hybrid – Bangalore, Pune, NCR
- 📅 Experience Required: 1–2 years (relevant experience)
If you already have some hands-on exposure to AI, APIs, or cloud platforms, this role can help you level up quickly.
🧠 What Will You Actually Work On?
Here’s what your day-to-day work might look like, explained in simple terms:
- 🤖 Helping build Generative AI applications like question-answering systems, summarization tools, and entity extraction engines.
- 🔗 Assisting in developing and debugging REST APIs using Python frameworks like FastAPI or Flask.
- 📚 Supporting RAG (Retrieval-Augmented Generation) pipelines – working with data ingestion, embeddings, and prompt testing.
- 🧩 Using vector databases such as Pinecone or FAISS for storing and retrieving AI data.
- ✍️ Experimenting with prompts and LLMs (like OpenAI or Mistral) under guidance.
- ☁️ Learning cloud deployment basics on AWS or Azure and helping with API or model deployments.
- 🔍 Exploring modern AI frameworks like LangChain and LangGraph.
This role is very hands-on, so if you enjoy building and experimenting, you’ll fit right in.
✅ Must-Have Skills (Don’t Panic, Read Carefully)
You don’t need to be an expert in everything. These are the expectations:
- 🐍 Python: Comfortable writing, testing, and debugging code.
- 🌐 API Development: Some experience with FastAPI, Flask, or similar frameworks.
- 🧠 GenAI Basics: Basic exposure to LLMs, prompt engineering, or simple RAG use cases.
- ☁️ Cloud Knowledge: Familiarity with AWS or Azure.
- 🔧 Git: Understanding version control and team collaboration.
If you meet most of these, you’re already in a good position.
⭐ Good-to-Have (Nice Bonus, Not Mandatory)
- 🔗 Hands-on exposure to LangChain or LangGraph
- 📊 Experience with embeddings or vector databases
- 🐳 Basic understanding of Docker or CI/CD pipelines
- 🤖 Curiosity about AI agents and autonomous systems
Even if you don’t have these yet, showing interest and learning mindset matters a lot.
📝 Preparation & Resume Tips (Important)
If you’re planning to apply, here’s a simple tip that helps many candidates:
- 📌 Highlight any AI, ML, or GenAI project you’ve worked on – even personal projects count.
- 📌 Mention tools like FastAPI, OpenAI APIs, embeddings, or cloud services clearly.
- 📌 Be honest. This role values learning ability more than pretending to know everything.
During interviews, expect basic Python questions, API concepts, and discussions around AI workflows rather than heavy math.
🚀 Career Growth & Future Scope
This role can be a strong foundation for growing into positions like AI Engineer, Applied ML Engineer, or GenAI Specialist. With GenAI demand growing fast, starting early in a role like this can really pay off.
📥 How to Apply (Free & Direct)
The application process is simple and completely free. You’ll be redirected to the official job page.
If you’re serious about building a future in AI, roles like this don’t come often. Take your time, prepare well, and apply confidently.
👉 One share can genuinely help someone start their AI career.
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