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