AI Engineer

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 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 and Data Processing: Python, SQL, Pandas, NumPy, Apache Spark

Tech Stack: MERN - React.js, Node.js

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