Senior Data Scientist

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Experience: - 5 to 10 Years

We are looking for an AI & Machine Learning Lead Engineer to lead the development and deployment of advanced AI and machine learning models. The ideal candidate will have a strong background in precision learning, large language models (LLMs), retrieval-augmented generation (RAG), natural language processing (NLP), and statistical methods for output analysis. This role involves leading a team of engineers and data scientists, setting strategic directions for AI/ML initiatives, and ensuring the delivery of impactful solutions that align with our business objectives.

Key Responsibilities:

• Leadership and Strategy:
• Lead and manage a team of AI and machine learning engineers, providing technical guidance and mentorship.
• Oversee the entire machine learning lifecycle, from data collection and preprocessing to model training, evaluation, and deployment.
• Develop and drive the AI/ML strategy, aligning it with overall business goals.
• Collaborate with cross-functional teams, including data engineering, product management, and software development, to integrate ML models into production environments.
• Precision Learning and Large Language Models (LLMs):
• Design, develop, and optimize precision learning algorithms for specific business applications.
• Lead efforts in developing, fine-tuning, and deploying large language models (LLMs) to address various use cases such as text generation, summarization, translation, and conversational AI.
• Retrieval-Augmented Generation (RAG):
• Implement and optimize RAG systems to improve the performance and accuracy of AI solutions.
• Develop retrieval strategies that effectively integrate large-scale knowledge bases with LLMs to generate more accurate and contextually relevant outputs.
• Natural Language Processing (NLP):
• Develop and implement NLP models for tasks such as text classification, sentiment analysis, named entity recognition, summarization, and question-answering.
• Stay current with NLP research trends and advancements, implementing best practices to enhance model efficiency and performance.
• Output Analysis through Statistical Methods:
• Analyse model outputs using advanced statistical methods to ensure reliability, accuracy, and explainability.
• Implement A/B testing, hypothesis testing, and other statistical techniques to validate model performance and derive actionable insights.
• Research and Development:
• Stay abreast of the latest research in machine learning, deep learning, and AI. Propose and implement novel approaches to solve challenging problems.
• Collaborate with academic and research institutions to contribute to the machine learning community through publications, open-source projects, and conferences
• MLOps and Model Deployment:
• Collaborate with DevOps and data engineering teams to implement MLOps practices for CI/CD pipelines, model monitoring, and governance.
• Ensure scalable deployment of AI/ML models on cloud platforms (AWS, GCP, Azure) or on-premises environments.
• Mentorship and Team Leadership:
• Mentor and guide a team of machine learning engineers and data scientists, fostering a culture of continuous learning and innovation.
• Conduct regular code reviews, provide constructive feedback, and ensure adherence to best practices in machine learning.

Required Qualifications:

• 8-10 years Experience with Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field; PhD is a plus.
• 5+ years of experience in machine learning and AI, with a strong focus on NLP, LLMs, RAG, and precision learning.
• Proven track record of leading AI/ML teams and projects from conception to deployment.
• Expertise in Python and relevant ML libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers.
• Strong understanding of NLP techniques, including transformer architectures (e.g., BERT, GPT).
• Experience in RAG techniques, knowledge retrieval systems, and integrating LLMs with external data sources.
• Proficiency in statistical analysis and hypothesis testing, with a strong foundation in experimental design.
• Excellent problem-solving skills, with the ability to articulate complex technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience with cloud services (AWS, GCP, Azure) for model deployment and scaling.
• Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines for ML.
• Knowledge of advanced AI techniques such as reinforcement learning, meta-learning, and unsupervised learning.
• Strong publication record or contributions to the AI/ML community.

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