Principal Engineer (AI) for Database Technology

Desired Qualifications

• 7+ years of overall technology experience in software engineering, enterprise architecture, platform engineering, database technology, machine learning, or AI-led transformation roles.
• 7+ years of experience in architecture and technical leadership, including defining technology strategy, target-state architecture, roadmaps, standards, and reusable engineering patterns for large enterprise platforms.
• 3+ years of hands-on experience in AI/ML, Generative AI, or intelligent automation, including architecting and delivering production-grade AI solutions that create measurable business value with integrations to enterprise approved AI tooling
• Strong expertise in Generative AI, Agentic AI, Machine Learning, LLM-based applications, RAG architectures, AI agents, knowledge systems, prompt engineering, model evaluation, and intelligent workflow automation.
• Proven experience designing and delivering enterprise-scale AI platforms and services, including reusable APIs, orchestration frameworks, developer toolkits, reference architectures, and common capabilities that accelerate adoption across teams.
• Deep understanding of cloud-native architecture, distributed systems, data platforms, API design, microservices, observability, scalability, reliability, and production deployment practices.
• Demonstrated experience partnering with executive leadership, business stakeholders, CIO organizations, architects, engineering teams, cybersecurity, risk, compliance, and governance partners to shape AI strategy and drive enterprise transformation.
• Strong knowledge of AI governance, responsible AI, model risk management, privacy, security, regulatory compliance, ethical AI principles, data protection, and operational risk controls.
• Experience evaluating and adopting modern AI technologies, frameworks, platforms, and vendors, including supporting build-vs-buy decisions, technology rationalization, cost optimization, scalability assessment, and total cost of ownership analysis.
• Ability to lead large, complex, cross-functional initiatives involving cloud platforms, data engineering, cybersecurity, enterprise applications, AI services, and automation platforms.
• Demonstrated ability to serve as a senior technical authority, providing architecture governance, design reviews, engineering guidance, mentoring, and thought leadership to senior engineers and architects.
• Excellent communication, influencing, and storytelling skills with the ability to translate complex AI concepts into clear business outcomes, architecture decisions, risks, and investment recommendations.
• Good understanding of database platforms like Oracle, PostgreSQL, MongoDB
• Job Expectations:
• Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related technical discipline.
• Experience building and deploying solutions using modern AI/ML and GenAI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, Hugging Face, MLflow, TensorFlow, PyTorch, or similar technologies.
• Hands-on experience with cloud AI services and platforms such as Devin AI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, Snowflake, Kubernetes, OpenShift, or other enterprise cloud platforms.
• Experience implementing agent orchestration patterns, multi-agent systems, workflow automation, tool/function calling, memory management, contextual retrieval, and human-in-the-loop controls.
• Strong understanding of LLMOps/MLOps practices, including model lifecycle management, prompt/version management, evaluation pipelines, guardrails, monitoring, drift detection, observability, and automated deployment.
• Experience designing AI solutions for highly regulated environments, preferably in financial services, banking, healthcare, insurance, or other risk-sensitive industries.
• Working knowledge of cybersecurity architecture, identity and access management, secrets management, data classification, encryption, secure SDLC, vulnerability management, and threat modeling for AI platforms.
• Experience with enterprise knowledge management, semantic search, vector databases, embeddings, metadata enrichment, data lineage, and retrieval optimization.
• Proven ability to establish engineering standards, architecture guardrails, reusable design patterns, coding best practices, and governance models for AI adoption at enterprise scale.
• Experience leading innovation initiatives, proof of concepts, technology incubations, hackathons, or AI transformation programs that move from experimentation to production adoption.
• Strong vendor management experience, including evaluating AI platforms, third-party tools, licensing models, commercial terms, platform fit, operational readiness, and long-term maintainability.
• Recognized thought leadership through whitepapers, internal architecture forums, patents, publications, conference talks, technical communities, or enterprise-wide engineering leadership.

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