AI Engineer – Data Science & Data Engineering

**Key Responsibilities** * Design, build, and optimize scalable data pipelines for AI/ML applications. * Develop, train, evaluate, and deploy Machine Learning and Deep Learning models. * Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases. * Fine-tune open-source and foundation models using domain-specific datasets. * Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management. * Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation. * Develop APIs and AI services for production deployment. * Collaborate with cross-functional teams to deliver scalable AI-driven solutions. * Monitor model performance, troubleshoot production issues, and maintain technical documentation. **Required Skills Mandatory** * 1–3 years of experience in Data Science, Data Engineering, or AI/ML development. * Strong programming skills in Python and SQL. * Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. * Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings. * Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation. * Experience in LLM fine-tuning and working with Hugging Face models. * Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD. * Experience with Git, REST APIs, Linux environments, and data processing libraries. **Preferred** * Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate. * Familiarity with Docker, Kubernetes, and MLflow. * Exposure to Apache Spark or Airflow for data engineering workflows. * Experience with cloud platforms (AWS, Azure, or GCP). **Primary Technology Stack** * Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark * AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn * Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n * Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings * Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models * Vector Databases: Pinecone, Chroma, Milvus, Weaviate * Databases: PostgreSQL, MongoDB * MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git * Cloud Platforms: AWS, Azure, GCP **Experience:** 1–3 Years **Domain:** Data Science | Data Engineering | Machine Learning | Generative AI | MLOps