AI-Native Delivery Lead & Agentic AI Engineering Lead

About Vericence
Vericence is a digital engineering and technology consulting firm helping enterprises build AI-driven platforms, modernize legacy systems, and scale innovation through cloud, data, and intelligent engineering. We partner with global organizations to deliver high-impact technology solutions and build world-class engineering teams.

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.

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.
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