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.