Data Scientist - Hybrid - Immediate Hiring - BI-ACDAA

Hello Everyone,

Greetings from Bounteous!!

We have an excellent Opening for the below Skills:

Job Title: Data Scientist

Experience: 5+ Years

Primary Skills: LLM (Fine-tuning/RAG) + Computer Vision (CNN/Transformers/Object Detection) + Python with PyTorch/TensorFlow + Production ML on Cloud (AWS/GCP/Azure)

Job Location: Bangalore

Kindly share the updated Profile to [email removed] along with the below details,

Current CTC:

Expected CTC:

Available Time to Join:

Preferred Location:

Job Description

Required Qualifications

• 5+ years of hands-on experience in data science, machine learning, or AI engineering, with at least 2 to 3 years in IC capacity.
• Deep expertise in LLMs fine-tuning (LoRA, QLoRA, PEFT), prompt engineering, RAG pipelines, embedding models, and LLM evaluation frameworks.
• Strong hands-on experience with Vision-Language Models (VLMs) such as LLaVA, GPT-4V, Gemini, or similar multimodal architectures.
• Proven track record in computer vision CNNs, transformers (ViT, DETR, SAM), object detection (YOLO, Faster R-CNN), segmentation, OCR, and video understanding.
• Solid command of classical ML techniques ensemble methods, gradient boosting (XGBoost, LightGBM), Bayesian methods, and time-series modeling.
• Strong proficiency in Python and ML/DL frameworks (PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or equivalent).
• Production ML experience building, deploying, and monitoring models in real-world systems with SLA requirements.
• Solid understanding of ML system architecture — feature engineering pipelines, model registries, CI/CD for ML, containerized deployments (Docker, Kubernetes).
• Experience with cloud platforms (AWS, GCP, or Azure) and GPU-accelerated training infrastructure.
• Proven ability to lead cross-functional technical teams, drive architecture decisions, and deliver projects on time.
• Excellent communication skills with the ability to translate complex AI concepts for diverse audiences.
• Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative field preferred.

Technical Depth Expected

• LLMs & NLP: Tokenization, attention mechanisms, transformer architectures, RLHF/DPO, vector databases (Pinecone, Weaviate, Milvus), chunking strategies, and retrieval-augmented generation.
• VLMs & Multimodal AI: Image-text alignment, contrastive learning (CLIP), multimodal embeddings, visual question answering, and document understanding models.
• Computer Vision: Image classification, object detection, instance/semantic segmentation, pose estimation, depth estimation, generative models (diffusion, GANs), and edge deployment (ONNX, TensorRT).
• Classical ML: Feature engineering, hyperparameter tuning, model selection, cross-validation, drift detection, and explainability (SHAP, LIME).
• Production Systems: Model optimization (quantization, pruning, distillation), A/B testing frameworks, shadow deployments, autoscaling inference endpoints, and latency profiling.