Base Salary
$123k - $200k/yr
Responsibilities
- Own the end-to-end architecture and development of AI-native products and platforms, including data pipelines and scalable production services.
- Implement generative AI systems using frontier LLMs, RAG pipelines, and multi-agent workflows.
- Lead rapid prototypes, proof-of-concepts, evaluation harnesses, and production hardening in partnership with product teams.
- Design and implement MLOps infrastructure covering model serving, automated testing, and observability.
- Research AI use cases such as threat-hunting copilots, developer productivity tools, and automated workflow optimization.
- Collaborate with product, design, and business stakeholders; mentor junior engineers; and promote responsible AI and experimentation.
- Present technical strategy, roadmaps, trade-offs, demonstrations, visualizations, and business impact to executives and engineering teams.
Requirements
- At least 10 years of professional experience in software engineering, machine learning engineering, or related fields, including at least 7 years of hands-on software or machine learning engineering and 3 years in technical leadership.
- Deep hands-on expertise with generative AI and LLM systems, including RAG, fine-tuning, agentic frameworks, prompt engineering, vector databases, and model evaluation.
- Strong backend engineering and distributed-systems fundamentals with mastery of Python.
- Hands-on experience with at least one major cloud AI platform, including GCP Vertex AI, AWS SageMaker, or Azure ML.
- Experience with containerized workflows using Docker and Kubernetes.
- Ability to translate ambiguous business problems into technical blueprints and phased execution plans.
- Exceptional written, verbal, communication, and presentation skills for engineering and senior-management audiences.
- B.S. or higher in Computer Science, Engineering, Mathematics, or a related technical field.
- Experience deploying AI into developer tooling, such as IDE plug-ins or CI/CD pipelines, is preferred.
- Active open-source AI/ML contributions, top-tier academic publications, and knowledge of combinatorial optimization or operations research are preferred.
Benefits
- Hybrid work schedule requiring four days in the office.
- Comprehensive benefits, allowances, bonuses, and market-competitive total compensation package.
- Extensive employee development opportunities and programs.
Tech Stack
Categories
About TSMC
The increased necessity of streamlining forces companies to apply new methods for maintenance organisation with a view to improving production efficiency. The experts from TSMC Production & Maintenance Consultants will work with you to chart your company. TSMC offers expert services founded on a combination of practical expertise and theoretical knowledge on maintenance and production efficiency. TSMC specialises in maintenance management and has wide experience in: Benchmark studies supported by "The International Study of Plant Reliability and Maintenance Effectiveness (RAM Study)"—a performance study from Solomon Associates Implementing computerised maintenance management systems (CMMS) Development and implementation of policies and strategies for maintenance Stock management, Registration of production efficiency and improvement of production results Preventive Maintenance Optimization Reliability Centred Maintenance Life Cycle Profit/ Life Cycle Costs TSMC utilises the principles, tools and methods available in TPM - Total Productive Maintenance and Uptime.