Senior security architect network and cloud

Principal AI Architect — Enterprise AI Delivery TeamLease Digital | New York / New Jersey, USA | Hybrid TeamLease Digital is seeking an experienced Principal AI Architect to lead enterprise AI architecture and production delivery for complex client environments, particularly within investment management and financial services. This is a senior technical leadership opportunity for a hands-on architect who can transform AI prototypes into scalable, secure, production-ready solutions while optimising performance and cost. Define enterprise AI architecture covering LLMs, RAG, agentic systems, model routing, orchestration, guardrails and evaluation. Lead open-weight model selection, fine-tuning, deployment and optimisation. Establish production engineering standards, automated testing, evaluation frameworks, CI/CD and release-readiness controls. Optimise AI inference costs, latency and performance through quantisation, caching, batching and distillation. Lead and mentor a team of 30+ AI engineers and data scientists across multiple client implementations. Partner with sales and consulting teams on solution architecture, technical proposals, feasibility assessments and cost estimation. 12+ years of experience across software engineering, architecture, data platforms and AI/ML. ~ Proven experience delivering enterprise AI systems into production for real users. ~ Hands-on experience fine-tuning and serving open-weight LLMs, including LoRA, QLoRA or full fine-tuning. ~ Strong Python, distributed systems, APIs, containers, Kubernetes, CI/CD and GPU-serving expertise. ~ Experience with a major cloud platform and data platforms such as Databricks or Snowflake. ~ Strong knowledge of AI evaluation, monitoring, governance and enterprise data readiness. ~ Demonstrated technical leadership while remaining hands-on with coding and architecture reviews. ~ A degree in Computer Science, Engineering or a related technical field. This role focuses on production-grade enterprise AI, not just prototypes or integrations with hosted AI APIs. Candidates should be able to demonstrate measurable improvements in inference cost, latency, model performance and production reliability.