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.
- 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.
- 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.
- 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.
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