Machine Learning Engineer Position

About the JobWe are seeking an Artificial Intelligence/Machine Learning Engineer to fill a pivotal role in our organization. As a key contributor, you will be responsible for designing and integrating cutting-edge machine learning models and algorithms, collaborating closely with cross-functional teams to recognize and establish project objectives.Key ResponsibilitiesModel Design and Integration: Develop, validate, and implement machine learning models and algorithms to drive business outcomes.Collaboration and Project Definition: Collaborate with data scientists and other stakeholders to understand and define project goals, ensuring alignment with organizational objectives.Data Infrastructure Management: Maintain data infrastructure and ensure scalability and efficiency of data-related operations, guaranteeing seamless data flow.Research and Development: Stay up-to-date with the latest developments in AI and propose their integration for operational enhancement, driving innovation within the organization.Communication and Stakeholder Management: Communicate complex data findings clearly and understandably to non-technical stakeholders, fostering effective collaboration and decision-making.Data Security and Governance: Adhere to data privacy and security guidelines, maintaining the integrity and confidentiality of sensitive information.Lifecycle Management: Participate in the entire AI project lifecycle, from concept to deployment and maintenance, ensuring successful project execution.Required Skills and QualificationsFoundational Knowledge: Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals, providing a solid foundation for advanced skills development.Experience with NLP and Vector Databases: Experience with mapping NLP models (BERT and GPT) to accelerators and awareness of trade-offs across memory, bandwidth, and compute, as well as experience with Vector databases like Qdrant, ChromaDB, Pinecone, PGVector or similar.Large Language Model Expertise: Experience with Large language Models like GPT 3.5, GPT 4, Llama, Gemini, Mistral etc.Agnent AI Workflows: Experience in design, build, and optimization of agentic AI workflows that enable autonomous reasoning, decision-making, and tool interaction using frameworks like LangChain, LangGraph, CrewAI, etc.Generative AI Solutions: Experience in implementation and fine-tuning Generative AI solutions (e.g., LLMs, multimodal models) for various business use cases.LLM Integration Frameworks: Experience with LLM integration framework like Llamaindex, Ragas, etcMachine Learning Model Development: Experience with ML Models from definition to deployment, including training, quantization, sparsity, model preprocessing, and deployment.Python Development: Proficiency in Python development in a Linux environment and using standard development tools.Deep Learning Frameworks: Experience with deep learning frameworks (such as PyTorch, Tensorflow, Keras, Spark).Artificial Neural Networks: Working knowledge of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).Computer Vision and Recommendation Systems: Experience in training, tuning, and deploying ML models for Computer Vision (e.g., ResNet), and/or Recommendation Systems (e.g., DLRM).Distributed System Deployment: Experience deploying ML workloads on distributed systems.Leadership and Communication: Self-motivated team player with a strong sense of ownership and leadership, possessing strong verbal, written, and organizational skills for effective communication and documentation.Research Background: Research background with a publication record.Cloud Computing and Information Security: Work experience at a cloud provider or AI compute/sub-system company, as well as knowledge of cloud computing platforms and services, such as AWS, Azure, or Google Cloud, and experience with information security and secure development best practices.Education and Experience: Bachelor's or higher degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field, and 2-4 yrs of hands-on experience in AI/ML],