Software Engineer (Entry-Level / Fresh Graduates)

Role Overview
We are looking for ambitious, high-potential fresh graduates and entry-level engineers to join our core team as a Software Engineer – AI / ML & Generative AI. This entry-level role is tailored for talented individuals who have a strong foundation in computer science and an eager passion to master modern artificial intelligence. You will receive hands-on mentorship while building, experimenting with, and deploying state-of-the-art Large Language Models (LLMs), deep learning networks, and generative AI systems into production software.
What You Will Do
Build Next-Gen AI Applications: Help integrate foundation models (OpenAI, Gemini, Llama, Claude) into real-world applications using orchestration tools like LangChain and LlamaIndex.
Develop Retrieval Systems: Work on Retrieval-Augmented Generation (RAG) pipelines and vector databases to empower AI models with custom knowledge and search capabilities.
Write Production-Ready Code: Build reliable backend microservices and APIs using modern Python frameworks to power AI-driven user experiences.
Learn MLOps Best Practices: Collaborate with senior engineers to track, evaluate, monitor, and deploy models using automated workflows and continuous integration tools.
Optimize AI Performance: Experiment with model fine-tuning techniques, prompt engineering, and low-latency inference setups to make our AI systems fast and cost-effective.
What We Are Looking For
Strong Programming Core: Solid proficiency in Python combined with a clear understanding of fundamental Data Structures, Algorithms, and Object-Oriented Programming principles.
AI & Machine Learning Knowledge: Firm academic or project-based grasp of core ML concepts, Deep Learning fundamentals (especially Transformers architecture), and basic experience with frameworks like PyTorch or TensorFlow.
Generative AI & LLM Curiosity: Hands-on experience through academic projects, internships, or personal repositories working with LLM APIs, prompt engineering, fine-tuning concepts, or RAG architectures.
Data Management: Working knowledge of SQL/NoSQL databases, data manipulation libraries (Pandas, NumPy), and vector search concepts (Pinecone, Chroma, Milvus, or Qdrant).
Development Basics: Familiarity with Git for version control, basic Linux environments, and exposure to containerization tools like Docker.
Bonus Points (Nice to Have)
Academic degrees in Computer Science, Data Science, AI, Mathematics, or related STEM fields.
Strong GitHub portfolio featuring completed AI, ML, or GenAI personal projects.
Prior internship experience in software development, data science, or AI engineering.
Contributions to open-source AI projects or participation in hackathons/Kaggle competitions.
Familiarity with emerging AI agent frameworks such as LangGraph, CrewAI, or AutoGen.

Work arrangement
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