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