AI-Native Delivery Lead & Agentic AI Engineering Lead / Architect (Data Engineering) - OCI

AI-Native Delivery Lead & Agentic AI Engineering Lead / Architect - OCI
Remote (US)
Full-Time / FTE Opportunity
A minimum 15 years of experience

Role Purpose:

• Lead the strategy, engineering, and end-to-end delivery of production-grade agentic AI capabilities for the OCI AIDP Lakehouse. This role combines portfolio execution, hands-on technical leadership, responsible AI governance, and organizational enablement to convert high-value business opportunities into secure, scalable, measurable AI solutions.
• Agent Factory, AI-Native Pod Execution, GenAI Engineering, and Guardrailed Delivery 

Job Description:

We are seeking a senior, hands-on AI-Native Delivery Lead & Agentic AI Engineering Lead to establish and lead an AI-native delivery practice for the OCI AIDP Lakehouse. The role owns the lifecycle of agentic AI initiatives—from opportunity discovery, value-case development, and solution architecture through engineering, deployment, adoption, and benefits realization. This leader will build reusable delivery patterns for AI-assisted software and data engineering, retrieval-augmented generation, vector search, tool-enabled agents, evaluation, observability, and human-in-the-loop controls.

What You Will Do:

AI-Native Delivery Leadership:

• Own the strategy, roadmap, operating model, and execution portfolio for AI-native and agentic AI initiatives aligned with enterprise priorities and measurable business outcomes.
• Lead multidisciplinary delivery pods spanning product, architecture, AI engineering, data engineering, platform engineering, security, governance, risk, and business operations.
• Translate ambiguous business needs into prioritized use cases, value hypotheses, delivery plans, acceptance criteria, adoption measures, and scalable production solutions.
• Establish delivery cadences, intake and prioritization practices, dependency management, executive reporting, release governance, and benefits-realization tracking.
• Define human-in-the-loop workflows in which AI accelerates analysis and engineering while accountable owners approve critical decisions and production releases.

Agent Factory and Engineering Acceleration:

• Build and operationalize an agent factory that accelerates pipeline development, transformation modeling, test creation, code review, documentation, metadata generation, and legacy modernization.
• Define reusable standards for context engineering, prompt and tool design, structured outputs, agent memory and state, model selection, orchestration, and secure integration with enterprise systems.
• Guide engineers in designing production-grade single-agent and multi-agent workflows using deterministic controls where appropriate and autonomous behavior only where risk and value justify it.
• Embed software-engineering rigor through version control, automated testing, CI/CD, threat modeling, observability, rollback, and production support.
• Create technical review practices that improve reuse, maintainability, cost efficiency, latency, resilience, and operational readiness.

AI Engineering and Consumption Patterns:

• Architect and deliver enterprise AI capabilities using large language models, embeddings, hybrid and vector search, retrieval-augmented generation, reranking, semantic grounding, tool calling, and agent orchestration.
• Integrate OCI Generative AI, Oracle AI Data Platform services, AI Vector Search, APIs, governed data products, and approved enterprise platforms into secure end-to-end workflows.
• Establish patterns for MCP-compatible tool access, identity-aware authorization, context assembly, memory management, structured outputs, and integration with operational applications.
• Define model and retrieval strategies based on accuracy, explainability, privacy, latency, availability, portability, and total cost of ownership.
• Partner with data architecture and governance teams to ensure AI experiences use certified definitions, trusted data products, lineage, and policy-aligned semantic context.

Responsible AI and Governance:

• Establish responsible AI controls covering use-case risk classification, privacy, security, fairness, transparency, human oversight, explainability, and accountable release approval.
• Implement evaluation frameworks for task success, groundedness, retrieval quality, hallucination, safety, refusal behavior, bias, latency, reliability, and cost.
• Require golden datasets, adversarial and regression testing, red-team scenarios, drift monitoring, audit logging, incident response, and documented release-readiness evidence.
• Ensure agents operate with scoped identities, least-privilege permissions, approved tools, bounded actions, retention controls, and complete traceability across prompts, context, decisions, and outputs.
• Partner with Security, Privacy, Legal, Risk, and Data Governance to maintain compliance in healthcare, claims-data, and PHI-sensitive environments.

What You Will Deliver:

• AI-native delivery strategy, portfolio roadmap, operating model, governance cadence, and measurable value framework.
• Reusable agent-factory architecture, engineering standards, reference implementations, context templates, and review workflows.
• Production patterns for RAG, vector and hybrid search, tool-enabled agents, multi-agent orchestration, and secure enterprise integration.
• Automated evaluation, observability, quality gates, release criteria, incident-management procedures, and responsible AI controls.
• Executive delivery dashboards covering value, adoption, cycle time, throughput, quality, reliability, risk, and cost.
• Documentation, playbooks, training, and knowledge-transfer assets that enable repeatable adoption across business domains.

Required Qualifications, Capabilities, and Skills:

• Bachelor’s degree in computer science, engineering, data science, information systems, or a related discipline, or equivalent practical experience; an advanced degree is a plus.
• Ten or more years of progressive experience in software, data, cloud, or AI engineering, including significant leadership of complex enterprise delivery.
• Demonstrated success taking Generative AI or machine-learning products from discovery and prototype through production deployment, adoption, and operational support.
• Deep hands-on knowledge of LLM application architecture, RAG, embeddings, vector and hybrid search, reranking, prompt and context engineering, tool calling, agent orchestration, and evaluation.
• Strong proficiency in Python, APIs, distributed-system design, automated testing, CI/CD, cloud-native deployment, security, telemetry, and production engineering.
• Experience leading cross-functional engineering pods, setting technical roadmaps, resolving dependencies, managing delivery risk, and communicating with senior executives and business stakeholders.
• Working knowledge of responsible AI, privacy, model risk, identity and access management, auditability, data governance, and controls for regulated environments.
• Ability to balance strategic leadership with hands-on architecture, technical review, coaching, and rapid problem solving.

Preferred Qualifications, Capabilities, and Skills:

• Hands-on experience with OCI Generative AI, Oracle AI Data Platform, Autonomous Database AI Vector Search, Select AI, OCI Data Science, or comparable enterprise cloud AI services.
• Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar orchestration frameworks, plus model evaluation and AI observability platforms.
• Knowledge of MCP, event-driven architectures, container platforms, Kubernetes, model gateways, prompt management, feature or vector stores, and LLMOps practices.
• Experience applying NIST AI RMF, ISO/IEC 42001, OWASP guidance for LLM applications, or comparable responsible AI and security frameworks.
• Healthcare payer, claims, clinical, financial-services, or other high-trust and audit-sensitive industry experience.
• Relevant Oracle Cloud, cloud architecture, AI/ML, security, product, or agile delivery certifications.

Success Measures:

• High-value AI use cases progress from intake to production with clear ownership, predictable delivery, and documented business outcomes.
• Agentic solutions meet agreed thresholds for accuracy, groundedness, safety, reliability, latency, adoption, and cost.
• Reusable patterns and self-service assets reduce cycle time and effort for subsequent domain onboarding.
• Delivery velocity improves without weakening architecture, security, privacy, data governance, compliance, or human accountability.
• Engineering teams adopt consistent AI-native practices, and stakeholders have transparent visibility into value, risk, quality, and operational health.

Engagement Model:

This principal-level role reports to the VP, Data Governance, AI & Analytics and operates within a small, senior AI-native delivery pod supporting the OCI AIDP Lakehouse and broader enterprise data-platform transformation. The role collaborates closely with platform and data architecture, data engineering, security and IAM, DevOps, governance, product, legal, risk, and business leaders, and is expected to influence enterprise standards while remaining close to engineering execution.