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GenAI Platform Engineer
4 weeks ago
Description : We are looking for a skilled Gen AI Platform Engineer to join our team. The ideal candidate will have 10 years of experience in managing LLM-based systems, with expertise in infrastructure management, prompt versioning, fine-tuning, and deployment. This role requires a strong understanding of GenAI workloads, performance tuning, scalability, and governance in cloud environments such as AWS, Azure, and Google Cloud. The engineer will play a pivotal role in optimizing the performance of AI systems and ensuring their scalability in production while building and deploying AI use cases and solutions using these platforms and tools.Key Responsibilities : - Manage and oversee the infrastructure for LLM-based systems, ensuring seamless operation and scalability.- Fine-tune, evaluate, and deploy prompt-based models, leveraging industry-standard tools and platforms.- Ensure the performance, scalability, and governance of GenAI workloads in cloud environments (AWS, Azure, Google Cloud).- Build and deploy AI use cases and solutions using the respective platforms and tools.- Collaborate with cross-functional teams to ensure effective deployment and performance optimization.- Lead the evaluation and enhancement of LLM-based models through iterative testing and fine-tuning.- Handle deployment pipelines, including CI/CD for LLM models.- Contribute to setting up automated processes for model fine-tuning and versioning.- Work on optimizing cloud-based infrastructure to support the growth of GenAI workloads.Required Skills : - Strong experience with cloud platforms such as AWS Sagemaker, Google Vertex, or Azure AI.- Proficiency in handling LLM systems, prompt fine-tuning, and versioning.- Hands-on experience with infrastructure management, model deployment, and optimization.- Strong understanding of cloud architecture, performance, and scalability for GenAI workloads.- Proficiency in Python, SQL, and Bash scripting.- Experience with machine learning frameworks such as Hugging Face, TensorFlow, PyTorch.- Familiarity with CI/CD pipelines, Docker, Kubernetes, and MLOps workflows.- Strong analytical skills and ability to troubleshoot complex infrastructure issues.Nice to Have Skills : - Familiarity with NLP frameworks and libraries such as Hugging Face, TensorFlow, PyTorch.- Experience in working with large-scale data processing frameworks like Apache Spark, Hadoop.- Knowledge of model explainability and interpretability techniques for LLMs.- Familiarity with containerization technologies (e.g., Docker, Kubernetes) for model deployment and orchestration.- Hands-on experience with MLOps pipelines.Soft Skills : - Strong communication skills to collaborate with cross-functional teams and stakeholders.- Problem-solving mindset with an ability to quickly address infrastructure or performance issues.- Attention to detail and a strong focus on quality and best practices.- Ability to work in a fast-paced, dynamic environment with changing priorities.- Strong analytical skills, with a focus on data-driven decision-making.Tools & Technical Skills : - Platforms : AWS Sagemaker, Google Vertex, Azure AI.- Tools : Docker, Kubernetes, Terraform, Jenkins (CI/CD), MLflow.- Languages : Python, SQL, Bash scripting.- Frameworks : Hugging Face, TensorFlow, PyTorch, Keras.- Databases : MySQL, PostgreSQL, NoSQL (MongoDB, Cassandra).- Other : Git, GitHub, Jenkins, CloudFormation.Education Qualification & Experience : - Education : A degree in Data Science, Computer Science, Engineering, or a related field. Masters degree is a plus.- Certification : Certification in AWS, Azure, or Google Cloud is preferred.- Experience : 4-6 years of relevant experience in AI/ML infrastructure management, with hands-on expertise in LLM systems, prompt versioning, and fine-tuning in a cloud environment. (ref:hirist.tech)