Position: AI Engineer

Role Overview:

We are looking for capable and self-driven entry-level AI Engineers who possess strong foundational knowledge in Artificial Intelligence and are ready to independently contribute to real-world projects from day one. This is not a trainee or support role. The candidate is expected to have hands-on knowledge and the ability to design, develop, and implement AI/ML solutions with minimal supervision.

Key Responsibilities:

• Design and implement machine learning models for business use cases such as prediction, classification, and automation.
• Develop and deploy AI-powered applications including chatbots, document processing systems, and recommendation engines.
• Work with structured and unstructured datasets, perform pre-processing, and build effective training pipelines.
• Build and integrate AI features into backend systems and APIs.
• Develop Generative AI solutions such as conversational assistants and knowledge-based systems.
• Optimize model performance and ensure scalability for production environments.
• Collaborate with cross-functional teams to deliver complete AI-enabled solutions.

Required Technical Competency (Must-Have Knowledge):

• Programming: Strong proficiency in Python. Ability to write clean, modular, and production-ready code.
• Core AI / ML: Solid understanding of Supervised & Unsupervised Learning, Regression, Classification, Clustering algorithms, and Model evaluation techniques (accuracy, precision, recall, etc.). Good understanding of Deep Learning basics (Neural Networks). Knowledge of Natural Language Processing (NLP) concepts.
• Frameworks & Libraries: Hands-on experience with at least one: TensorFlow / PyTorch / Scikit-learn. Experience using Pandas, NumPy for data processing.
• Generative AI & Modern AI Skills (Mandatory): Practical understanding of Large Language Models (LLMs). Academic Experience in Prompt engineering and Building AI chatbots or assistants. Familiarity with tools/frameworks such as OpenAI APIs / Hugging Face / LangChain. Understanding of RAG (Retrieval-Augmented Generation) concepts. Awareness of vector databases (FAISS, Pinecone, etc.).
• Backend, Data & Integration Skills: Strong SQL knowledge (PostgreSQL / MySQL / Oracle). Ability to build and consume REST APIs. Understanding of backend frameworks (Flask / FastAPI / Spring Boot). Knowledge of integrating AI models into microservices architecture.
• Deployment & Tools: Basic understanding of Model deployment concepts, Docker (containerization), Cloud basics (AWS / Azure / GCP), and Version control: SVN or Git.

Project Expectations (Very Important):

• Candidates must have completed hands-on projects demonstrating: End-to-end ML model development, At least one AI/ML or NLP-based application, Preferably one Generative AI or chatbot project.