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About the Role
We are looking for an experienced Data Scientist with strong hands-on expertise in Machine Learning, Deep Learning, GenAI, NLP, and Computer Vision. The ideal candidate will work on building, fine-tuning, deploying, and optimizing AI/ML solutions, including LLM-based applications, AI agents, RAG pipelines, and computer vision models.
You will work closely with engineering and cross-functional teams to take AI solutions from experimentation to production.
Key Responsibilities
Design, develop, train, and optimize Machine Learning and Deep Learning models.
Work with large-scale image, video, and text datasets, including data preprocessing and augmentation.
Develop solutions using Python, PyTorch, TensorFlow, and other modern ML frameworks.
Work with Transformers, RNNs, Transfer Learning, Vision Transformers (ViT), and fine-tuning techniques.
Build and optimize NLP and Generative AI solutions.
Develop RAG pipelines, agentic workflows, and AI agents using frameworks such as LangGraph.
Design and optimize prompt engineering workflows for LLMs such as GPT, Claude, and LLaMA.
Implement and optimize Computer Vision models, including CNN-based architectures and modern vision models.
Apply MLOps best practices, including model versioning, CI/CD, automated deployment, monitoring, and model lifecycle management.
Collaborate with engineering teams to productionize AI/ML models and applications.
Follow enterprise-level AI security, governance, compliance, and software development best practices.
Analyze model performance and continuously improve accuracy, scalability, and efficiency.
Required Skills
4+ years of hands-on experience in Data Science / Machine Learning / AI.
Strong programming skills in Python.
Hands-on experience with PyTorch and/or TensorFlow.
Strong understanding of Machine Learning and Deep Learning concepts.
Experience with NLP, Transformers, LLMs, and Generative AI.
Experience building RAG pipelines and agentic AI workflows.
Hands-on experience with AI Agent frameworks, preferably LangGraph.
Experience with prompt engineering and LLM fine-tuning.
Strong understanding of Computer Vision, image/video processing, and deep learning architectures.
Experience with MLOps, model deployment, versioning, and CI/CD pipelines.
Strong knowledge of data preprocessing, feature engineering, model evaluation, and optimization.
Experience working with Databricks and Azure.
Good understanding of Linux/UNIX environments and command-line tools.
Understanding of enterprise AI security, governance, and compliance.
Good to Have
Experience with CNNs, Reinforcement Learning, and Vision Transformers (ViT).
Experience working with multimodal AI models.
Experience with model optimization and inference performance.
Experience with cloud-based AI/ML platforms.
Experience mentoring junior Data Scientists or ML Engineers.
Work arrangement
No
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