About This Role

5 to 8 years of experience
Skills: Generative AI , Python , LLM , GPT

Senior Data Scientist — AI/ML Tech Lead

Own the full AI lifecycle — from research and modeling to production-grade architecture and team leadership

Level

Senior / Tech Lead (8+ years experience)

Tech Stack

Flexible / Cloud-agnostic

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

- 8+ years of hands-on experience in data science, machine learning, or AI engineering, with at least 2–3 years in a tech lead or senior IC capacity.

- Deep expertise in LLMs — fine-tuning (LoRA, QLoRA, PEFT), prompt engineering, RAG pipelines, embedding models, and LLM evaluation frameworks.

- Solid 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.

📌 Data Scientist (India)
🏢 Bounteous
📍 India