Senior Machine Learning Engineer/AI Specialist - Atlanta

- 5-7+ years of demonstrable experience writing code in one or more primary programming languages (Python, Java, etc.) - Over 1 year of experience implementing vector search, semantic search, or embedding-based retrieval systems for operational ML or AI applications - More than 1 year of experience working with typeahead/autocomplete systems and incorporating ML signals into query comprehension or ranking workflows - At least 1 year of experience merging outputs from multiple retrieval systems (e.g., vector search, typeahead, personalization models) to enhance relevance - Over 1 year of experience deploying ML and GenAI/LLM models while addressing scalability, accuracy, and maintainability - Practical experience with ML frameworks and libraries (Scikit-learn, Pytorch, Tensorflow, LightGBM, Keras, MLFlow, etc.) as well as familiarity with LLM-specific frameworks (e.g., LangChain, Hugging Face Transformers) - Hands-on experience with one or more cloud services for ML and GenAI/LLM (Amazon SageMaker, Amazon Bedrock, Databricks Mosaic AI, Seldon, Arize, etc.) - Minimum of 4 years of experience designing and constructing diverse software architectures, particularly scalable architectures that support both traditional ML and advanced LLM workflows - Profound understanding of data structures, distributed computing, and core software engineering principles - Over 3 years of providing technical leadership within teams, managing projects, and defining technical directions for initiatives - Familiarity with relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis, Terraform, Airflow) - Capability to convey findings in easily digestible formats, such as dashboards or data modeling - Engage in regular design process evaluations and maintain development standards within the team - Collaborate with leadership to promote the acceptance of ML and GenAI/LLM solutions among product engineering teams - Experience in a cloud setting like AWS, Google Cloud Platform, or Azure - Familiarity with Databricks and its unity catalog is a bonus - Experience designing and constructing data pipelines for production-level ML and GenAI/LLM infrastructure - Exposure to vector databases for effective management and retrieval of embeddings for LLM applications, facilitating high-performance similarity search and retrieval-augmented generation (RAG) processes is a plus - Inspire junior engineers to adopt best practices and contemporary industry design patterns.