Principal/Staff Machine Learning Engineer (Generative AI + LLM)

Company Overview:
10Pearls is an end-to-end digital technology services partner helping businesses utilize technology as a competitive advantage. We help our customers digitalize their existing business, build innovative new products, and augment their existing teams with high-performance team members. Our broad expertise in product management, user experience/design, cloud architecture, software development, data insights and intelligence, cyber security, emerging tech, and quality assurance ensures that we are delivering solutions that address business needs. 10Pearls is proud to have a diverse clientele including large enterprises, SMBs and high-growth startups. We work with clients across industries, including healthcare/life sciences, education, energy, communications/media, financial services, and hi-tech. Our many long-term, successful partnerships are built upon trust, integrity and successful delivery and execution.

Requirements:
We are looking for a “Principal/Staff Machine Learning Engineer”. The ideal candidate should have a Master’s degree in Computer Science with 5 – 8 years of developing machine learning models, with a strong portfolio in Computer Vision and LLMs.

Responsibilities:

• Lead the design, development, and deployment of generative AI models, large language models, and retrieval-augmented generation systems.
• Conduct cutting-edge research in AI, contributing to advancements in image and video analysis, object detection, segmentation, and NLP.
• Collaborate with product teams to integrate AI/ML technologies into new and existing products.
• Develop and implement machine learning algorithms and models using state-of-the-art techniques and best practices.
• Optimize models for performance, scalability, and efficiency on cloud platforms.
• Implement MLOps practices to streamline the machine learning lifecycle, including model training, deployment, monitoring, and maintenance.
• Mentor and lead a team of machine learning engineers, fostering a culture of technical excellence.
• Optimize machine learning workflows for improved model performance and efficiency.
• Develop and maintain robust data pipelines for model training and inference at scale.
• Implement rigorous model testing and validation to ensure high-quality deployments.
• Contribute to the company's intellectual property through innovative research, patents, and publications.
• Work closely with cross-functional teams, including data scientists, analysts, and other developers, to understand data requirements and implement effective solutions.
• Stay abreast of industry trends and emerging technologies in AI to maintain a competitive edge.
• Communicate technical concepts effectively to stakeholders and influence strategic decisions with ML insights.

Requirements:

• Advanced degree (Ph.D. or Master's) in Computer Science, Machine Learning, or a related field
• 5+ years of experience in machine learning and deep learning, with a focus on generative AI and large language models.
• Experience with NLP and text generation models such as GPT, Gemini, LLaMA, or BERT.
• Experience with LLamaIndex or Langchain for efficient indexing and retrieval of large language model data, optimizing the performance of generative AI systems.
• Strong programming skills in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with cloud platforms, specifically AWS (SageMaker & Bedrock), GCP (Vertex AI), and Azure (Machine Learning).
• Knowledge of MLOps tools and practices, including CI/CD, model versioning, and deployment automation.
• Demonstrated ability to lead and mentor a team of machine learning engineers.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Proficiency in data modeling, data pipeline development, and big data technologies.
• Track record of innovation and thought leadership in the field of AI, evidenced by publications, patents, or conference presentations.
• Excellent problem-solving skills and the ability to work in a fast-paced, dynamic environment.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Commitment to continuous learning and staying current with the latest ML research and technologies

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