**Any work authorization is fine**nn**Location is Ashburn, VA**nn**Experience level is SENIOR**nn**Responsibilities:**nn * Design and develop end-to-end applications that seamlessly integrate machine learning capabilities, including real-time inference, batch processing, and efficient data management to deliver scalable and robust solutions.n * Identify bottlenecks in the model development, deployment, and monitoring process.n * Design and implement production-ready machine learning pipelines, including model training, validation, deployment, and monitoring (e.g., labelled data sets to check performance of prompts).n * Build scalable, high-performance infrastructure to support Generative AI workflows (e.g., distributed training, inference optimization, and GPU/TPU utilization).n * Deploy GenAI applications into production cloud environments with performance, cost, and latency trade-offs considered (e.g., open-source vs. closed-source, quantization, prompt token length, completion caching, prompt caching).n * Monitor and troubleshoot model performance, addressing issues such as performance drift and response latency.n * Stay at the forefront of Generative AI advancements, identifying opportunities to incorporate the latest research and techniques into production systems.nn**Qualifications:**nn * Bachelor's or advanced degree in computer science, engineering, or a related field.n * 3+ years of experience in machine learning engineering, with a focus on deploying AI systems at scale.n * Experience working with large-scale Generative AI applications in production environments.n * Relevant experience in the legal domain is a plus.n * Strong proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorch).n * Experience with Generative AI tools and techniques (e.g., LLMs, quantization, synthetic data generation, knowledge distillation, retrieval-augmented generation, fine-tuning).n * Proficiency in commons GenAI libraries (e.g., LangChain, Autogen) and cloud-native AI services (e.g., Azure search)n * Knowledge of cloud infrastructure (e.g., Azure) and management tools for IT components, storage, networking, and caching.n * Familiarity with ML Ops principles, including CI/CD pipelines, containerization, and automated testing for AI systems.n * Experience with modern container platforms (e.g., Docker, OpenShift) and tools like Jenkins, Git, and Sonar.n * Strong problem-solving skills with the ability to address complex technical challenges.n * Excellent communication skills to collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders.n * Eagerness to stay updated with cutting-edge AI research and apply innovative ideas to real-world problems.n * Organization and attention to detail, ensuring high-quality delivery.n * Ability to work collaboratively to create innovative and efficient solutions. Responsibilities: Design and develop end-to-end applications with machine learning capabilities.
Identify bottlenecks in model development, deployment, and monitoring processes.
Implement production-ready machine learning pipelines.
Build scalable infrastructure for Generative AI workflows.
Deploy GenAI applications into production cloud environments.
Monitor and troubleshoot model performance.
Incorporate latest AI research and techniques into production systems.