AI/ML 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 SparkAI & Machine Learning: PyTorch, TensorFlow, Scikit-learnApplication Frameworks: LangChain, LlamaIndex, CrewAI, n8nCore Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, EmbeddingsModels & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding ModelsVector Databases: Pinecone, Chroma, Milvus, WeaviateDatabases: PostgreSQL, MongoDBMLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, GitCloud Platforms: AWS, Azure, GCPExperience: 1–3 YearsDomain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps