Artificial Intelligence Engineer

Location: Bengaluru

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 (1B–7B 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, YOLO
11, YOLO
26) 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 Lang Graph, CrewAI, or Auto Gen

• 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 Hugging Face 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:
Lang Graph, CrewAI, or Auto Gen

• 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 Prompt Foo

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