AI & Machine Learning Lead Engineer
5 days ago
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 Bachelor’s or Master’s 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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