AI Engineer

Design, develop, and deploy AI and machine learning solutions for real-world business use cases.

Build and optimize RAG-based architectures including document ingestion, chunking, embedding generation, vector search, retrieval pipelines, and response generation.

Work with large language model platforms and APIs such as OpenAI, Anthropic Claude, and Google Gemini.

Implement NLP solutions for tasks like text classification, summarization, entity extraction, semantic search, and question-answering systems.

Develop machine learning models addressing forecasting, classification, and regression problems.

Fine-tune prompts to enhance model responses while evaluating output quality for accuracy and reliability.

Collaborate with product managers, engineers, and data scientists to convert business requirements into AI-powered features.

Optimize AI pipelines focusing on performance metrics such as cost efficiency, latency reduction, scalability, and maintainability.

Evaluate model performance using appropriate validation techniques and metrics.

Stay current with emerging trends in Generative AI, LLM operations (LLMOps), vector databases, and applied machine learning methodologies.

1-3 years of hands-on experience in AI, machine learning, data science, or related engineering roles.

Solid understanding of large language models such as OpenAI, Claude, and Gemini.

Experience building or working with Retrieval-Augmented Generation (RAG) systems.

Proficiency in Python programming for AI development.

Familiarity with NLP techniques and libraries like Hugging Face, spaCy, or NLTK.

Knowledge of machine learning algorithms used for forecasting, classification, and regression tasks.

Experience working with vector databases such as Pinecone, FAISS, Chroma, or Weaviate.

Skills in data preprocessing, feature engineering, training models, evaluating outputs, and deploying solutions.

Understanding of APIs and REST services for integrating AI models into applications.

Strong problem-solving abilities along with debugging and analytical skills.

Preferred experience with AI orchestration frameworks like LangChain or LlamaIndex is a plus.

Familiarity with cloud platforms such as AWS, Azure or Google Cloud is advantageous.

Exposure to MLOps or LLMOps practices including model monitoring and versioning is beneficial.

Experience handling SQL databases and unstructured data sources such as PDFs or websites is helpful.

Basic knowledge of Docker containers, CI/CD pipelines, and scalable deployment practices adds value.

Bachelor’s degree in Computer Science or related technical fields.A