Role Overview :

Lead the end-to-end design, development, and deployment of AI/ML solutions across LLM, VLM, computer vision, and traditional ML domains.

Key Responsibilities :

- 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.