Trainee AI Engineer

We are seeking fresh 2026 graduates with a consistent academic record of 85% or above throughout their education.

Education: 

• BE. / B. Tech with Computer Science or IT
• MCA
• B. Sc. / M. Sc. (IT)

Work Mode: 5 days WFO 

Job Description: 

As an AI Engineer, you’ll be part of a next-generation engineering team building intelligent, autonomous systems that combine Machine Learning, Large Language Models (LLMs), and Agentic reasoning frameworks. You’ll participate end-to-end — from data ingestion and model training to Retrieval-Augmented Generation (RAG), agent orchestration, and deployment of scalable microservices. This role demands curiosity, precision, and a passion for bridging traditional ML and modern LLM-based agentic architectures into production-grade systems. 

Key Responsibilities 

•         Design and implement RAG pipelines for large-scale data retrieval and contextual reasoning.
•         Build, train, and fine-tune ML models using Scikit-learn, TensorFlow, orPyTorchfor structured and unstructured datasets. 
•         Develop modular AI microservices (FastAPI/Flask) integrated with vector databases (FAISS,ChromaDB, Elasticsearch) and embedding pipelines.
•         Orchestrate multi-agent systems usingLangGraph,LangChain, or Model Context Protocol (MCP) for dynamic task execution. 
•         Integrate and evaluate LLM APIs (OpenAI, Azure OpenAI,Groq, Anthropic, etc.) for reasoning, generation, and workflow automation.
•         Implement data and ML pipelines (Airflow,ZenML,Dagster) for training, evaluation, and inference. 
•         Develop and expose REST or WebSocket APIs for model and agent communication.
•         Apply prompt engineering and reranking strategies to improve contextual accuracy and system reasoning.
•         Work closely with data, backend, and AI engineers to deploy, scale, and monitor intelligent microservices in test/staging environments.
•         Contribute to shared AI infrastructure modules, ensuring reproducibility, maintainability, and performance.
•         Research and prototype new architectures, including multimodal RAG, structured reasoning, or fine-tuning pipelines.

Required Technical Skills 

•         Strong understanding of RAG architecture — document chunking, embedding, retrieval, reranking, and synthesis.
•         Proficiency in Python, including libraries like Pandas,Numpy, and Scikit-learn.
•         Experience withFastAPIor Flask for microservice and API development. 
•         Hands-on withLangGraph,LangChain, or MCP for multi-agent orchestration. 
•         Familiarity with vector databases (FAISS,ChromaDB,Weaviate, Elasticsearch). 
•         Good Working knowledge of ML/DL frameworks (TensorFlow,PyTorch).
•         Understanding of model serving via REST APIs, batch jobs, or pipelines.
•         Exposure to containerization (Docker) and scalable architecture patterns.
•         Familiarity with Git,MLflow, or DVC for versioning, tracking, and collaboration.
•         Awareness of cloud AI/ML services (Azure ML, AWS SageMaker, GCP Vertex AI).
•         Understanding of prompt evaluation, retrieval accuracy metrics, and contextual reasoning benchmarks.

Secondary Skills 

•         Curiosity to explore Agentic AI frameworks and autonomous reasoning systems.
•         Analytical mindset with a focus on debugging, optimization, and scalability.
•         Strong communication and documentation skills for collaborative workflows.
•         Innovative and research-driven approach toward new AI architectures.
•         Eagerness to experiment, iterate, and learn from production-level systems.