BI-AAEEF - Data Scientist (Classical ML + CV) - Hybrid - Bangalore

Hello Everyone,

Greetings from Bounteous!!

We have an excellent Opening for the below Skills:

Job Title: Data Scientist

Experience: 5+Years

Primary Skills: Classic ML + Computer Vision

Job Location: Bangalore/Gurugram

Work Mode: 3 Days WFO

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

Current CTC:

Expected CTC:

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Job Description

Overview

We are looking for a Senior Data Scientist with strong tech lead capabilities to spearhead our AI and machine learning initiatives end to end. This is not a pure research role you will architect, build, and ship production-grade AI systems while mentoring a multidisciplinary team of data scientists and ML engineers. The ideal candidate brings deep, hands-on expertise across Large Language Models (LLMs), Vision-Language Models (VLMs), Computer Vision (CV), and classical ML, combined with a strong understanding of system architecture, scalable infrastructure, and MLOps. You will serve as the technical authority on all things AI from selecting the right model architecture to designing the serving layer and ensuring reliability in production.

Key Responsibilities

• Lead the end-to-end design, development, and deployment of AI/ML solutions across LLM, VLM, computer vision, and traditional ML domains.
• Define and own the AI/ML technical architecture model pipelines, training infrastructure, feature stores, and serving systems.
• Build and fine-tune Large Language Models (LLMs) and Vision-Language Models (VLMs) for domain-specific applications (e.g., RAG, agents, multimodal understanding).
• Design and deploy production-grade computer vision systems (object detection, segmentation, OCR, video analytics) at scale.
• Develop and maintain classical ML models (regression, classification, clustering, time-series forecasting, recommendation engines) where appropriate.
• Architect scalable, low-latency model serving infrastructure for both batch and real-time inference workloads.
• Establish best practices for experiment tracking, model versioning, A/B testing, reproducibility, and documentation across the team.
• Mentor and technically lead a team of data scientists and ML engineers; conduct architecture reviews, design discussions, and code reviews.
• Collaborate closely with Product, Engineering, and Business teams to translate complex business problems into well-defined AI solutions.
• Evaluate and integrate emerging AI technologies, frameworks, and research papers into the product roadmap.
• Champion responsible AI fairness, bias detection, explainability, and compliance with data privacy regulations.
• Communicate technical strategies, trade-offs, and results to senior leadership and non-technical stakeholders.

Required Qualifications

• 7+ years of hands-on experience in data science, machine learning, or AI engineering.
• 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.

Nice to Have

• Experience building agentic AI systems (tool use, multi-step reasoning, autonomous workflows).
• Familiarity with MLOps platforms (MLflow, Kubeflow, Weights & Biases, Vertex AI, SageMaker).
• Publications, patents, or significant open-source contributions in AI/ML.
• Experience with synthetic data generation and data augmentation strategies.
• Background in reinforcement learning or optimization techniques.
• Exposure to edge AI / on-device model deployment (mobile, IoT).
• Experience working in fast-paced startup or high-growth environments.
• Knowledge of data governance, model risk management, and regulatory compliance frameworks.