Machine Learning Engineer II

Essential Functions:

• Designs, develops, and deploys machine learning models for real-world applications.
• Builds scalable pipelines for data ingestion, pre-processing, training, and inference.
• Owns end to end development of machine learning algorithms including data analysis, feature engineering, model development, training, validation, and performance evaluation.
• Designs, implements, and optimizes retrieval-augmented generation (RAG) pipelines that combine large language models (LLMs) with vector search/retrieval systems.
• Builds data ingestion and embedding pipelines for efficient indexing and retrieval.
• Fine-tunes and adapts LLMs for domain-specific tasks such as instruction tuning, prompt engineering, low-rank adaptation (LoRA), etc.
• Engages in both engineering and research, exploring latest ML algorithms, solution architectures, and cutting-edge approaches to improve retrieval and generation performance.
• Works with stakeholders to translate business requirements into robust technical solutions that deliver measurable impact.
• Works with engineering teams to continuously scale and advance machine learning across the organization.
• Identifies new opportunities of applying ML technology to improve business workflows and processes.
• Builds a deep understanding of the company’s products, services, data, and customers to deliver impactful solutions.
• Performs other related duties and projects as business needs require at direction of management.

Education and Experience:

• Master’s degree in Machine Learning, Deep Learning, or a computer science-related field. PhD preferred.
• Minimum three (3) years of relevant work experience in the areas below, or any equivalent education and/or experience from which comparable knowledge, skills and abilities have been demonstrated/achieved:
• Understands fundamental concepts, practices, and procedures of machine learning field.
• Data discovery, data aggregation, and feature engineering with SQL query writing skills.
• Training, evaluating, optimizing, deploying, and maintaining machine learning models on production systems.
• Logging, tracking, A/B testing, evaluating and analyzing the performance of different machine learning algorithms and models in production.
• Strong development skills in Python programming language and have experience in developing data-driven, scalable, and reliable applications with Amazon Web Services (AWS).
• Applying machine learning algorithms to solve a wide range of optimization problems like customer sales prediction, recommendation engine, sentiment analysis, deep learning with image and natural language, customer segmentations/clustering, and object detection.
• Utilization of popular open-source machine learning/deep learning libraries like LangChain, HuggingFace, Tensorflow, scikit-learn, pandas, pyTorch, and Keras.
• Experience working with relational, non-relational, and high-scale data processing and storage frameworks like Structured Query Language (SQL), AWS RedShift, Aurora, S3, DynamoDB, MySQL, PostgreSQL.
• Experience with AWS Serverless architecture and AWS native services like BedRock, EC2, Lambda, Step Functions, SageMaker, Rekognition, Comprehend, Lex/Polly, and Transcribe.

Pay Range: $121,108.84.84 - $146,000/YR