GenAI Engineer (Fresher)
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Posted 2h ago
๐ Job Description
About the Role
We are looking for a passionate and enthusiastic GenAI Engineer (Fresher) to join our AI team. You will work on building and integrating Generative AI applications using Large Language Models (LLMs), Python, and modern AI frameworks. This is an excellent opportunity for candidates eager to learn and work on cutting-edge AI technologies.
Responsibilities
- Develop AI-powered applications using Python.
- Build and integrate LLM-based solutions using frameworks such as LangChain.
- Create prompt engineering strategies to improve AI responses.
- Develop REST APIs using FastAPI or Flask.
- Work with vector databases and Retrieval-Augmented Generation (RAG) pipelines.
- Integrate AI models with web applications and enterprise systems.
- Test, debug, and optimize AI applications.
- Collaborate with senior engineers, product teams, and stakeholders.
- Stay updated with the latest advancements in Generative AI and machine learning.
Requirements
Required Skills
- Strong knowledge of Python programming.
- Basic understanding of Large Language Models (LLMs) and Generative AI concepts.
- Knowledge of Prompt Engineering.
- Understanding of REST APIs.
- Familiarity with Git and version control.
- Good analytical and problem-solving skills.
- Strong communication and willingness to learn.
Preferred Skills
- Exposure to LangChain, Langfuse, or similar frameworks.
- Basic knowledge of RAG, embeddings, and vector databases (e.g., ChromaDB, Pinecone).
- Experience with FastAPI or Flask.
- Familiarity with cloud platforms such as Azure, AWS, or GCP.
Educational Qualification
- B.E./B.Tech in Computer Science, Information Technology, Artificial Intelligence, Data Science, or related fields.
- Recent graduates (2025/2026 pass-outs) are encouraged to apply.
Nice to Have
- Internship or academic projects in AI/ML or Generative AI.
- Certifications in Python, AI, Machine Learning, or LLMs.
- GitHub portfolio showcasing AI projects.
- Participation in hackathons or open-source contributions.