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.

nThe 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.

nKey Responsibilities:

nStrategic AI Leadership:

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- Define and drive enterprise AI strategy focused on scalable, production-grade AI systems.
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- Lead architecture design for AI platforms leveraging LLMs, RAG pipelines, and agentic systems.
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- Partner with executive and business leaders to align AI initiatives with long-term transformation goals. (Engineer_A...ration_NEW | Word)
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nAI Architecture & Solution Design:

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- Architect end-to-end AI solutions integrating LLM APIs, orchestration frameworks (LangChain, LlamaIndex), and vector databases.
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- Establish design standards, frameworks, and reusable components for AI development.
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- Ensure systems are robust, secure, highly available, and optimized for performance at scale.
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nEnterprise Integration & Delivery:

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- Oversee integration of AI solutions into enterprise systems and workflows.
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- Drive adoption of AI-powered automation across business functions.
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- Lead complex, high-impact AI initiatives spanning multiple teams and geographies.
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nTechnical Leadership & Mentorship:

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- Provide technical leadership and mentorship to engineers, guiding best practices in AI/ML development.
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- Drive engineering excellence through code quality, design reviews, and innovation.
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- Build organizational capability in emerging AI technologies and platforms.
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nInnovation & Thought Leadership:

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- Stay ahead of emerging AI trends, including GenAI, agentic AI, and automation frameworks.
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- Champion innovation and experimentation while balancing risk, governance, and compliance.
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- Represent the organization in technical forums, architecture discussions, and innovation councils.
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nAI Performance & Governance:

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- Define KPIs for AI systems (accuracy, scalability, latency, business impact).
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- Implement monitoring and continuous improvement mechanisms.
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- Ensure responsible AI practices including ethical use, bias mitigation, and data security.
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nQualifications & Experience:

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- Bachelors/Masters degree in Computer Science, AI, Engineering, or related field.
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- 10+ years of overall experience, with robust expertise in AI/ML, system architecture, and enterprise integrations.
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- Proven track record of delivering large-scale, production-grade AI solutions.
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📌 Principal Engineer, AI (Hyderabad)
🏢 TMUS Global Solutions
📍 Hyderabad