AI/ML & Generative AI Engineer
SUMMARY
We are looking for an AI/ML & Generative AI Engineer responsible for developing, fine-tuning, evaluating, optimizing, integrating, and deploying AI models across Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, and Machine Learning. The candidate should be capable of working with both pretrained open-source models and custom-trained models to build production-ready AI solutions.
TECHNICAL SKILLS
Python
Machine Learning
Deep Learning
Generative AI
Large Language Models
NLP
Computer Vision
PyTorch and/or TensorFlow
Hugging Face Transformers
OpenCV
YOLO
NumPy
Pandas
REST APIs
Git/GitHub
Hugging Face Datasets
PEFT
LoRA
QLoRA
SFT
Tokenization
Prompt engineering
Quantization
Model evaluation
GPU-based training
Google Colab / cloud GPU environments
Embeddings
Vector databases
Semantic search
Document chunking
Retrieval pipelines
Prompt context management
RAG evaluation
FAISS
Chroma
Qdrant
Pinecone
LangChain or LlamaIndex
Ollama
vLLM
FastAPI
Docker
Linux
Cloud/GPU deployment
SQL/NoSQL databases
Model monitoring
MLOps fundamentals
JOB TITLE
AI/ML & Generative AI Engineer
ROLE OVERVIEW
We are looking for an AI/ML & Generative AI Engineer responsible for developing, fine-tuning, evaluating, optimizing, integrating, and deploying AI models across Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, and Machine Learning.
The candidate should be capable of working with both pretrained open-source models and custom-trained models to build production-ready AI solutions.
KEY RESPONSIBILITIES
LLM & Generative AI: - Work with pretrained Large Language Models such as Gemma, Llama, Mistral, Qwen, and similar open-source models. - Fine-tune LLMs using organization-specific or domain-specific datasets. - Prepare and validate instruction datasets for supervised fine-tuning. - Implement Supervised Fine-Tuning (SFT). - Work with parameter-efficient fine-tuning techniques such as LoRA and QLoRA. - Use PEFT techniques to customize large models efficiently. - Work with quantized models to reduce memory and computational requirements. - Evaluate fine-tuned models for accuracy, relevance, hallucination, safety, and response quality. - Build prompt templates and structured model outputs. - Implement model inference pipelines.
RAG & Knowledge-Based AI: - Develop Retrieval-Augmented Generation (RAG) applications. - Implement document ingestion and chunking pipelines. - Generate and manage embeddings. - Work with vector databases. - Implement semantic search and similarity retrieval. - Integrate organizational documents and knowledge bases with LLM applications. - Improve retrieval quality and generated responses. - Develop conversational AI and question-answering systems.
Computer Vision: - Train and fine-tune Computer Vision models. - Work with YOLO and other detection/classification architectures. - Develop object detection, classification, segmentation, and tracking solutions. - Process image, video, CCTV, and real-time streaming data. - Use OpenCV for image/video processing. - Evaluate and improve Computer Vision model accuracy and inference speed.
Machine Learning & Deep Learning: - Build and train Machine Learning and Deep Learning models. - Perform data preprocessing and feature engineering. - Conduct model evaluation and hyperparameter tuning. - Analyze model performance and identify improvement opportunities. - Conduct experiments with different models and training configurations.
Model Deployment & Integration: - Build APIs for AI model inference using frameworks such as FastAPI or Flask. - Integrate AI models with web applications and backend systems. - Containerize AI applications using Docker. - Deploy models on GPU servers or cloud infrastructure. - Optimize models for production inference. - Implement model versioning and monitoring. - Maintain reproducible training and deployment pipelines.
EXPECTED CAPABILITIES
The candidate should be able to take an AI requirement through the complete lifecycle: Requirement → Data Preparation → Model Selection → Training/Fine-Tuning → Evaluation → Optimization → API Integration → Deployment → Monitoring
The engineer should also be comfortable researching and experimenting with new AI models and techniques depending on project requirements.
EXPERIENCE
0–2 years of practical experience in AI/ML, Generative AI, LLMs, NLP, Computer Vision, or related development.
Strong practical projects involving LLM fine-tuning, RAG, Computer Vision, or production AI integration can be considered in place of extensive professional experience.
Pay: ₹413,643.11 - ₹1,706,294.96 per year
Work Location: In person
