Director- Software Engineering Lead

About the Company

Responsibilities

• ● Define and lead a multi-year technical vision for AI applications and platforms, aligning technology choices to business outcomes and total cost of ownership.
• ● Establish and carry out architecture principles, reference designs, and patterns for full-stack AI solutions to ensure reliability, security, and scalability.
• ● Provide technical leadership across a portfolio of AI initiatives; review solution designs, mediate trade-offs, and ensure value delivery through stage gates and measurable objectives.
• ● Drive shared components, APIs, model/prompt registries, feature stores, RAG services, and internal developer tooling to accelerate delivery and standardize quality.
• ● Lead model selection frameworks, evaluation methods, prompt governance, guardrails, and human-in-the-loop patterns for LLMs, agents, and retrieval systems.
• ● Own technical standards for SLOs, resilience, observability, and cost efficiency; guide guidelines for model and service performance monitoring and continuous improvement in production.
• ● Embed security, privacy, and ethical AI principles into building and delivery, including bias testing, explainability, and provenance; collaborate with Security, Legal, and Compliance on governance and audits.
• ● Translate strategy into roadmaps and reference implementations with Product, Data Science, and Platform Engineering; influence adoption through technical guidance rather than line authority.
• ● Mentor and upskill senior engineers and technical leads; cultivate communities of practice and technical learning programs to amplify impact.
• ● Evaluate and guide strategic technology partners and platforms, setting technical guardrails, SLAs, and cost controls.
• ● Scan emerging technologies, run targeted pilots, and transition validated innovations into production-ready standards.

Requirements

• ● 15+ years in software/AI engineering with a strong record of technical leadership; experience guiding managers and senior engineers through technical influence rather than formal management.
• ● Bachelor's or Master's in Computer Science, Engineering, or related field (or equivalent experience); advanced degree preferred.
• ● Deep experience designing distributed, cloud-native systems and microservices; strong understanding of data architectures and API ecosystems.
• ● Confirmed expertise with ML and GenAI solution patterns at scale, including model lifecycle management, ML Ops, and proven ability to guide teams in delivering AI responsibly.