Applyđź“Ť Austin, Texas, United States; Atlanta, GA, United States
🧠Full-Time
🔍 Construction technology, Legal tech
- Education: Bachelor’s in Computer Science, Machine Learning, or a related field.
- Technical Skills: Proficiency in Python; Strong understanding of NLP algorithms, including transformer architectures and large language models; Extensive experience with RAG systems, knowledge graphs, vector databases, and semantic search technologies; Advanced knowledge of cloud platforms (AWS, Azure, Google Cloud) and their ML services; Expertise in LLM prompt engineering, fine-tuning, and evaluation.
- Experience: Multiple years of professional experience in AI/ML related engineering roles; Proven track record of leading complex AI/ML projects from conception to production; Extensive experience with model deployment, MLOps, and maintaining ML systems in production, or equivalent experience with ML systems integration at scale; Demonstrated ability to translate business requirements into technical solutions.
- Leadership and Soft Skills: Strong leadership and team management skills; Excellent problem-solving and analytical abilities; Outstanding communication skills, able to explain complex technical concepts to diverse audiences; Proven ability to influence and drive decision-making across teams and departments.
- Lead AI/ML Strategy and Implementation: Understand and execute the company's AI/ML roadmap, aligning with business objectives; Design, implement, and deploy advanced RAG systems to enhance document understanding and analysis capabilities; Lead the integration of cutting-edge AI/ML solutions into existing products and services; Work closely with DevOps engineers and cross-functional teams to integrate AI/ML solutions into existing workflows.
- Advanced AI/ML Research and Development: Spearhead research initiatives on state-of-the-art AI/ML algorithms and technologies; Design and conduct experiments to solve challenging problems in document analysis and automation; Work independently to ideate, research, prototype, and experiment with novel RAG stack solutions.
- ML Architecture and Systems Design: Architect scalable, efficient, and maintainable ML systems and pipelines; Optimize ML system integration performance and resource utilization; Design and implement ML monitoring and observability systems.
- Team Leadership and Mentorship: Serve as an experienced source of ML knowledge to less experienced or non-technical cross-functional team members; Foster a culture of innovation, continuous learning, and best practices within the team; Collaborate with cross-functional leaders to align AI initiatives with product and business strategies.
- Continuous Improvement and Innovation: Stay at the forefront of AI/ML advancements and industry trends; Identify and evaluate new technologies and methodologies for potential adoption; Drive the adoption of best practices in AI systems evaluation and benchmarking.
AWSLeadershipPythonMachine LearningStrategyAlgorithmsAzureCommunication Skills
Posted 2024-09-28
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