AI Engineer

WorkOnGrid; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

WorkOnGrid in Bengaluru is looking for an AI Engineer to develop intelligent systems leveraging LLMs and agentic workflows. This role focuses on integrating AI into operational frameworks for real-time decision-making and workflow automation.

The ideal candidate will have experience in Data Science, Machine Learning, Computer Vision, and AI/ML Ops. Proficiency in Python and frameworks like FastAPI is preferred, along with expertise in scalable AI systems.

Qualifications

• Strong experience in building Data Science and ML applications.
• Expertise in developing Agentic AI applications with orchestration frameworks.
• Solid experience in Computer Vision applications.

Responsibilities

• Architect scalable AI systems end-to-end.
• Strong debugging and troubleshooting skills.
• Experience with performance optimization of low-latency AI systems.

Skills

Data Science and Machine Learning applications

Agentic AI applications

Computer Vision applications

AI/ML Ops (AIOps/MLOps)

Large Language Models (LLMs)

Voice AI systems

Tools

Python

FastAPI

Flask

PyTorch

TensorFlow

Docker

Kubernetes

AWS

GCP

Azure

Job description

We are looking for an AI Engineer to build AI-native capabilities within GRID, moving beyond traditional automation to intelligent, context-aware systems powered by LLMs, RAG pipelines, and agentic workflows. You will play a key role in shaping how AI integrates with operational systems, enabling real-time decisioning, workflow automation, and intelligent insights at scale.

Requirements

• Strong experience in building and developing Data Science and Machine Learning applications, including: Regression models, Predictive analytics, Time series forecasting, and Model evaluation and optimization.
• Hands‑on expertise in developing Agentic AI applications using modern orchestration frameworks such as LangChain, LangGraph, Deep Agents, Multi‑agent workflows, and autonomous systems.
• Solid experience in Computer Vision applications, including: Object detection, OCR systems, Image processing pipelines, Real‑time vision inference systems.
• Strong understanding of AI/ML Ops (AIOps/MLOps), including: Model development lifecycle, Training pipelines, CI/CD for AI systems, Containerization and orchestration, Cloud/GPU deployment, Monitoring, scaling, and production optimization.
• Deep expertise in Large Language Models (LLMs), including LLM deployment and serving, Cost optimization and inference efficiency, Context engineering and prompt orchestration, RAG pipelines and vector databases, Fine‑tuning and evaluation strategies.
• Experience integrating Voice AI systems, including: Speech‑to‑Text (STT), Text‑to‑Speech (TTS), Real‑time audio streaming, Voice agent architectures, and Conversational AI systems.

Preferred Technical Stack

• Python, FastAPI, Flask, Async Programming.
• PyTorch / TensorFlow.
• Docker, Kubernetes, jenkins.
• Redis, Kafka, celery, Vector Databases.
• AWS / GCP / Azure.
• WebRTC / WebSocket‑based streaming systems.

Additional Expectations

• Ability to architect scalable AI systems end-to-end.
• Strong debugging and production troubleshooting skills.
• Experience handling performance optimization and low‑latency AI systems.
• Ability to work across research, engineering, and deployment layers.
• Strong communication and technical leadership skills.