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.
- Robust 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

Skills:- Python, Kubernetes, Docker, TensorFlow, PySpark, PyCharm, Data engineering, Data Science, Weaviate, Scikit-Learn, NumPy, pandas, Large Language Models (LLM), LLM Evaluation Frameworks, Generative AI, Huggingface, n8n, SQL, LangChain, Pinecone, Vector database, LlamaIndex, Retrieval Augmented Generation (RAG), ChromaDB, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning

📌 AI/ML Engineer – Data Science & Data Engineering (Bengaluru)
🏢 TopGrep Tech Private
📍 Bengaluru