Junior AI Engineer

Job Title:

Junior AI Engineer Location:

Onsite | Full-Time Industry:

Artificial Intelligence / Software Development Experience:

1–2 years

About the Role Our client is a fast-growing technology company building AI-powered products for enterprise customers across industries. They are looking for a passionate AI Engineer to join their team and work on developing, deploying, and optimizing AI-driven solutions that solve real-world business problems.

Role Overview As an AI Engineer, you will work on building and integrating machine learning and Generative AI solutions into scalable applications. You will collaborate with product managers, data engineers, and backend developers to design intelligent systems that enhance automation, decision-making, and user experience.

This role is ideal for someone with 1–2 years of hands-on experience in AI/ML development and a strong interest in building production-ready AI systems.

Key Responsibilities Develop, test, and deploy machine learning and AI models for real-world applications Build and integrate LLM-based features (e.g., chatbots, summarization, RAG pipelines) Work with structured and unstructured data for training and fine-tuning models Implement APIs and backend services to serve AI models in production Optimize model performance, latency, and scalability Collaborate with cross-functional teams in an Agile development environment Participate in code reviews, experimentation, and model evaluation

Required Skills 1–2 years of hands-on experience in AI/ML development Strong proficiency in Python Experience with ML frameworks such as TensorFlow or PyTorch Understanding of NLP concepts and LLM integrations Familiarity with REST APIs and backend development concepts Experience working with vector databases or embeddings (preferred exposure) Strong problem-solving skills and analytical thinking Good communication skills

Nice to Have Experience with Generative AI frameworks (e.g., LangChain or LangGraph) Exposure to cloud platforms like AWS, Azure, or GCP Familiarity with Docker and basic DevOps concepts Experience with knowledge graphs or graph databases Understanding of RAG (Retrieval-Augmented Generation) pipelines

Education Bachelor’s or Master’s Degree in Computer Science, Engineering, Data Science, or a related field