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Chief Architect
3 weeks ago
Drive the future of responsible AI innovation by leading the design, development, and delivery of cutting-edge AI safety services and platforms. As AI accelerates, safety can't be an afterthought.
The Head of Engineering – AI Safety Services plays a critical role in building the guardrails that let innovation move fast without breaking trust. It's not just about scaling technology — it's about scaling it responsibly.
Key Responsibilities- Lead and mentor a cross-functional team of AI engineers, platform engineers, and a product manager.
- Set clear goals, promote collaboration, and cultivate a culture of impact and rapid learning.
- Implement agile processes to ensure speed, security, and quality in development.
- Partner closely with clients to understand their AI safety goals, challenges, and integration needs.
- Translate customer requirements into actionable roadmaps aligned with innovation cycles.
- Oversee the creation of tools for alignment assessment, adversarial testing, drift detection, interpretability analysis, and make strategic build-vs-buy decisions to accelerate customer success.
- Implement and enforce best practices in secure coding, automation, monitoring, and infrastructure-as-code.
- Maintain high system reliability through SLAs/SLOs, rigorous testing, and operational dashboards.
- Stay updated on advancements in AI safety, model evaluation, and adversarial robustness.
- Pilot and integrate emerging techniques to improve AI deployment speed and safety.
- Must-Have:
- 5+ years in software engineering, including 2+ years in a leadership role.
- Proven experience delivering AI/ML or data-intensive platforms.
- Strong background in coaching and talent development.
- Excellent communication and cross-functional collaboration skills.
- Languages/Frameworks: Python, TensorFlow, PyTorch, JavaScript, HTML/CSS.
- Tools: Docker, Kubernetes, REST APIs, SQL/NoSQL databases.
- Cloud Platforms: AWS, GCP, or Azure.
- Understanding of LLMs, prompt engineering, and adversarial testing.
- Familiarity with LangChain, LangGraph.
- Experience with distributed systems (e.g., Spark, Flink).
- Knowledge of AI regulatory frameworks (e.g., SOC 2, ISO 27001).