Accountabilities

• Own the machine learning infrastructure supporting governance models, including model serving, evaluation, monitoring, red-teaming, and continuous improvement.
• Build and maintain low-latency model-serving systems and evaluation pipelines designed for production workloads.
• Develop and manage model registries and supporting infrastructure for reliable model lifecycle management.
• Translate research and experimental models into production systems, including optimization techniques such as quantization and distillation to achieve low-latency inference.
• Build drift-monitoring and shadow-deployment capabilities to identify changes in model behavior and validate new versions safely.
• Develop telemetry-to-training pipelines that turn production signals and audit data into actionable inputs for model improvement.
• Design and maintain red-team testing infrastructure, including attack orchestration, scorecards, regression suites, and CI-based quality gates.
• Establish continuous self-improvement workflows covering dataset curation, retraining, validation, and controlled model rollout.
• Implement appropriate safeguards and deployment controls to support secure and auditable machine learning operations.
• Collaborate across engineering and AI-focused teams to advance infrastructure for AI agents, governance, trust, and retrieval capabilities.
• Contribute to a high-quality engineering culture through documentation, testing, monitoring, and continuous improvement.
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Requirements

• 4–7 years of professional experience in machine learning software engineering, with hands-on experience building LLM-based or agentic systems.
• Strong production experience with ML model serving, particularly low-latency inference, quantization, and model distillation.
• Proven experience building evaluation pipelines and working with model registries.
• Experience designing red-team or adversarial testing frameworks, including regression testing and CI-gated workflows.
• Practical experience with model drift monitoring and shadow deployment strategies.
• Strong SQL skills and solid working knowledge of PostgreSQL.
• Security-first mindset and experience developing or operating systems in audited, compliance-sensitive, or security-conscious environments.
• Strong software engineering and problem-solving skills, with the ability to take ML systems from experimentation through reliable production deployment.
• Ability to design scalable infrastructure and establish robust processes for model evaluation, monitoring, and continuous improvement.
• Strong communication and collaboration skills within distributed, cross-functional engineering environments.
• Experience building automated retraining loops using production telemetry or audit data is a plus.
• Familiarity with Model Context Protocol (MCP), tool registries, and AI agent orchestration patterns is an advantage.
• Experience developing PostgreSQL extensions in C or Rust, or contributing to the PostgreSQL ecosystem, is a plus.

Accountabilities

• Own the machine learning infrastructure supporting governance models, including model serving, evaluation, monitoring, red-teaming, and continuous improvement.
• Build and maintain low-latency model-serving systems and evaluation pipelines designed for production workloads.
• Develop and manage model registries and supporting infrastructure for reliable model lifecycle management.
• Translate research and experimental models into production systems, including optimization techniques such as quantization and distillation to achieve low-latency inference.
• Build drift-monitoring and shadow-deployment capabilities to identify changes in model behavior and validate new versions safely.
• Develop telemetry-to-training pipelines that turn production signals and audit data into actionable inputs for model improvement.
• Design and maintain red-team testing infrastructure, including attack orchestration, scorecards, regression suites, and CI-based quality gates.
• Establish continuous self-improvement workflows covering dataset curation, retraining, validation, and controlled model rollout.
• Implement appropriate safeguards and deployment controls to support secure and auditable machine learning operations.
• Collaborate across engineering and AI-focused teams to advance infrastructure for AI agents, governance, trust, and retrieval capabilities.
• Contribute to a high-quality engineering culture through documentation, testing, monitoring, and continuous improvement.
• 
  

Requirements

• 4–7 years of professional experience in machine learning software engineering, with hands-on experience building LLM-based or agentic systems.
• Strong production experience with ML model serving, particularly low-latency inference, quantization, and model distillation.
• Proven experience building evaluation pipelines and working with model registries.
• Experience designing red-team or adversarial testing frameworks, including regression testing and CI-gated workflows.
• Practical experience with model drift monitoring and shadow deployment strategies.
• Strong SQL skills and solid working knowledge of PostgreSQL.
• Security-first mindset and experience developing or operating systems in audited, compliance-sensitive, or security-conscious environments.
• Strong software engineering and problem-solving skills, with the ability to take ML systems from experimentation through reliable production deployment.
• Ability to design scalable infrastructure and establish robust processes for model evaluation, monitoring, and continuous improvement.
• Strong communication and collaboration skills within distributed, cross-functional engineering environments.
• Experience building automated retraining loops using production telemetry or audit data is a plus.
• Familiarity with Model Context Protocol (MCP), tool registries, and AI agent orchestration patterns is an advantage.
• Experience developing PostgreSQL extensions in C or Rust, or contributing to the PostgreSQL ecosystem, is a plus.