Note: The job is a remote job and is open to candidates in USA. 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. The AI-Native Delivery Lead & Agentic AI Engineering Lead will lead the strategy, engineering, governance, and end-to-end delivery of production-grade agentic AI capabilities for the OCI AIDP Lakehouse, while establishing reusable delivery patterns and enabling organizational adoption.
Responsibilities
- 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
- 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
- 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
- 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
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
- • 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
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