Principal Engineer, AI

The Principal Engineer, AI Applications Integration is a senior technical leadership role responsible for defining and driving enterprise AI architecture, strategy, and large-scale deployment of intelligent systems. This role leads the design of advanced AI ecosystems, including LLM-powered applications, agentic workflows, and RAG frameworks, enabling enterprise-wide transformation, automation, and decision intelligence. The Principal Engineer acts as a thought leader, influencing cross-functional leaders, mentoring engineering teams, and ensuring AI solutions are scalable, secure, and aligned to business outcomes.

Key Responsibilities: Strategic AI Leadership: Define and drive enterprise AI strategy focused on scalable, production-grade AI systems. Lead architecture design for AI platforms leveraging LLMs, RAG pipelines, and agentic systems. Partner with executive and business leaders to align AI initiatives with long-term transformation goals. (Engineer_A...ration_NEW | Word) AI Architecture & Solution Design: Architect end-to-end AI solutions integrating LLM APIs, orchestration frameworks (LangChain, LlamaIndex), and vector databases.

Establish design standards, frameworks, and reusable components for AI development. Ensure systems are robust, secure, highly available, and optimized for performance at scale. Enterprise Integration & Delivery: Oversee integration of AI solutions into enterprise systems and workflows.

Drive adoption of AI-powered automation across business functions. Lead complex, high-impact AI initiatives spanning multiple teams and geographies. Technical Leadership & Mentorship: Provide technical leadership and mentorship to engineers, guiding best practices in AI/ML development.

Drive engineering excellence through code quality, design reviews, and innovation. Build organizational capability in emerging AI technologies and platforms. Innovation & Thought Leadership: Stay ahead of emerging AI trends, including GenAI, agentic AI, and automation frameworks.

Champion innovation and experimentation while balancing risk, governance, and compliance. Represent the organization in technical forums, architecture discussions, and innovation councils. AI Performance & Governance: Define KPIs for AI systems (accuracy, scalability, latency, business impact).

Implement monitoring and continuous improvement mechanisms. Ensure responsible AI practices including ethical use, bias mitigation, and data security. Qualifications & Experience: Bachelors/Masters degree in Computer Science, AI, Engineering, or related field. 10+ years of overall experience, with strong expertise in AI/ML, system architecture, and enterprise integrations.

Proven track record of delivering large-scale, production-grade AI solutions.