Job Opening for AI Engineer Fresher (Job Code RT 1612)

Job Title: AI Engineer Fresher
Working Model: Work from Office – 5 Days
Location: Pune, Mumbai
Budget: ₹4 LPA – ₹6 LPA (as per interview performance)
Bond Duration: 2 Years
Open Positions: 5
Education: BE / B.Tech – AI/ML specialization only
Percentage Criteria: 85% and above in 10th, 12th, and Graduation (2025/2026 batch)

Role Overview

As an AI Engineer, you will 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 will 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 with modern LLM-based agentic architectures in 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, or PyTorch for structured and unstructured datasets.

• Develop modular AI microservices (FastAPI/Flask) integrated with vector databases (FAISS, ChromaDB, Elasticsearch) and embedding pipelines.

• Orchestrate multi-agent systems using LangGraph, 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 Pandas, NumPy, and Scikit-learn.

• Experience with FastAPI or Flask for microservice and API development.

• Hands-on experience with LangGraph, LangChain, or MCP for multi-agent orchestration.

• Familiarity with vector databases (FAISS, ChromaDB, Weaviate, Elasticsearch).

• Working knowledge of ML/DL frameworks (TensorFlow, PyTorch).

• Understanding of model serving via REST APIs, batch jobs, or pipelines.

• Exposure to containerisation (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, optimisation, 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