DataScientist

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) Function Data Science & AI Engineering Reports To 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. • 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.