LatentBridge | Data Scientist | pune

5 days ago

pune, India LatentBridge Full time

Job Description:


Experience: - 8+ 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 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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