Artificial Intelligence Engineer

Emmvee Photovoltaic Power Limited - AI / ML Engineer

About the Role

We are looking for a motivated AI/ML Engineer to work across LLM and SLM training, visual defect detection using YOLO models, and multi-agent system development. You will own the full lifecycle from data

preparation and model training to production deployment, with a focus on building reliable, efficient, and domain-specific AI systems.

Key Responsibilities

LLM & SLM Training

Fine-tune large and small language models (LLMs & SLMs) on domain-specific datasets using SFT, LoRA, and QLoRA

Train lightweight SLMs (1B7B parameters) for specific use cases such as defect classification, report generation, anomaly summarisation, and structured data extraction

Apply preference alignment techniques RLHF and DPO to align model outputs with task requirements

Build and maintain RAG pipelines to ground model responses in domain knowledge

Evaluate models against task-specific benchmarks and iterate on training data quality

Optimise inference using vLLM, TGI, or llama.cpp for cost-efficient production serving

Visual Defect Detection YOLO Models

Train and fine-tune YOLO models (YOLOv8, YOLO11, YOLO26) for defect detection, segmentation, and classification

Build annotation pipelines and manage image datasets using Roboflow or Label Studio

Optimise models for edge and CPU deployment using TensorRT, ONNX, or OpenVINO

Develop monitoring and retraining workflows to handle real-world data drift in production

Multi-Agent System Development

Design and build multi-agent workflows using LangGraph, CrewAI, or AutoGen

Define agent roles, implement tool use and function calling, and manage state across agent turns

Integrate agents with external APIs, databases, and internal services

Build evaluation and oversight mechanisms for agent reliability and safety in production

MLOps & Deployment

Package and deploy models as REST APIs using FastAPI, containerised with Docker

Track experiments and model versions with MLflow or Weights & Biases

Set up cloud-based training and serving pipelines on AWS, GCP, or Azure

Maintain documentation model cards, data sheets, and experiment logs

Skills & Qualifications

Must Have

Bachelor's or Master's in Computer Science, AI, Data Science, or equivalent

Strong Python skills; proficient with PyTorch and the HuggingFace ecosystem (Transformers, PEFT, TRL, Datasets)

Experience fine-tuning LLMs or SLMs end-to-end data prep, training, evaluation, and deployment

Hands-on experience training YOLO-family models for detection or segmentation tasks

Familiarity with multi-agent frameworks: LangGraph, CrewAI, or AutoGen

Working knowledge of RAG, vector databases, and prompt engineering

Comfortable with Git, Docker, and basic cloud infrastructure

Good to Have

Experience training SLMs from scratch or distilling larger models into smaller ones

Knowledge of multimodal models (vision + language) such as LLaVA or Qwen-VL

Exposure to model quantisation (AWQ, GPTQ) and edge deployment workflows

Familiarity with evaluation frameworks: RAGAS, lm-evaluation-harness, or PromptFoo

Open-source contributions or a public portfolio of AI/ML projects