rganization ParyAI Solutions Pvt Ltd

Job ID JID025017

Designation Industrial AI Engineer

Experience

Minimum 2 years of experience in SLM and Python

programming

Qualification Graduate/ Postgraduate in any field

Desired skills

• Problem-solving mindset for debugging hardwaresoftware integration issues

• Self-starter capable of researching the latest papers in

SLM and Quantization

Job location Raghuvanahalli, Kanakapura Road, Bengaluru, Karnataka

CTC 4.0-6.0 LPA

Contact number 9008981927 / 8147928988

Email id hr@paryai.ai /arjun@paryai.ai

Website www.paryai.ai

• We request interested candidates to send resume through mail to the above

mentioned mail IDs only

Please mention above mentioned “Job ID” in the mail subject

Job Role – Industrial AI Engineer

We are looking for a skilled Python Programmer with a strong background in Machine

Learning and Natural Language Processing (NLP). Your primary objective will be to

design, fine-tune, and deploy Small Language Models (SLMs)

SLM Development & Fine-Tuning

• Select appropriate open-source Small Language Models (e.g., Microsoft Phi-3,

Mistral, Llama-3 8B, Gemma) suitable for edge deployment or on-premise servers.

• Fine-tune models using our proprietary operational manuals, historical sensor

logs, and troubleshooting guides to create a domain-expert AI.

• Implement RAG (Retrieval-Augmented Generation) pipelines to allow the AI to

access real-time PLC/SCADA data and static documentation simultaneously.

Python Programming & Backend

• Develop robust Python microservices (FastAPI/Flask) to host the model and serve

inference requests.

• Write efficient scripts for data pre-processing: cleaning time-series data from

sensors and converting it into textual context for the SLM.

Integration

• Build interfaces to ingest data from industrial protocols (Modbus, MQTT, OPC UA).

• Ensure the SLM allows for "Chat-with-your-Plant" functionality

Optimization & Edge Deployment

• Optimize models for low-latency inference using quantization (GGUF, AWQ) and

ONNX runtime.

• Ensure the system can run on edge devices (e.g., Industrial PCs) with limited

compute resources.