Oracle AI & OCI Engineer / Developer

Job Description

Position Summary

\n

Serve as the hands-on technical lead responsible for designing, coding, integrating, securing, deploying, and operating Oracle AI solutions and agency Proofs-of-Value (PoVs). The role combines GenAI/agentic application development, Oracle AI Database and data engineering, AI security and responsible-AI controls, and OCI DevSecOps/platform engineering. The successful candidate is expected to lead by building—writing production-quality code, creating data pipelines, configuring Oracle/OCI services, automating infrastructure and deployments, troubleshooting issues, and mentoring engineers through implementation.

\n

Core Hands-on Responsibilities

\n

\n
• Lead development of secure GenAI, RAG, and agentic AI applications using OCI Generative AI, approved models, enterprise data, APIs, tools, and workflows.
\n
• Personally develop Python services, REST APIs, prompts, tool integrations, guardrails, evaluation logic, error handling, and lightweight demonstration interfaces.
\n
• Build RAG pipelines including document ingestion, chunking, enrichment, embeddings, vector indexing/search, metadata filtering, reranking, grounding, and citation/source traceability.
\n
• Design and implement governed AI agents with identity, authorization, approved tool access ,workflow orchestration, and human approval where required.
\n
• Configure and optimize Oracle Database / Oracle AI Database for vector storage, indexing, similarity search, metadata, retrieval, SQL/PLSQL processing, and RAG/agent workloads.
\n
• Engineer structured and unstructured ETL/ELT and ingestion pipelines connecting Oracle databases, enterprise applications, files, APIs, and approved external data sources.
\n
• Implement data quality, lineage, masking, retention, access control, environment separation, and synthetic/de-identified data patterns for PoVs.
\n
• Provision and maintain OCI environments using Infrastructure-as-Code; develop reusable Terraform modules and standardized development/deployment patterns.
\n
• Build CI/CD pipelines for AI applications, APIs, data pipelines, infrastructure, and configuration; manage Git, artifacts, versions, secrets, promotion, and rollback.
\n
• Implement containers and, where applicable, Kubernetes/OKE deployment patterns, secure networking, IAM, connectivity, logging, metrics, tracing, observability, and cost monitoring.
\n
• Embed security-by-design into AI solutions, including IAM/authorization, encryption, secrets, API security, audit logging, threat modeling, vulnerability testing, and secure SDLC practices.
\n
• Test LLM-specific risks including prompt injection, data leakage, hallucination, unsafe tool use, authorization bypass, groundedness, refusal behavior, and citation traceability.
\n
• Instrument AI applications for latency, retrieval quality, model/token usage, errors, task completion, security events, and operational KPIs.
\n
• Own technical troubleshooting and performance optimization across application, model integration, database/vector retrieval, data pipeline, OCI platform, and deployment layers.
\n
• Package reusable accelerators, source-code templates, IaC modules, ingestion components, evaluation suites, test data, deployment automation, and operational runbooks.
\n
• Lead code/design reviews and mentor engineers while remaining directly accountable for working code and demonstrable technical outcomes.
\n
• Take PoVs through agency demonstration and technical hardening into pilot/production-ready implementations, including deployment, security remediation, documentation, and operational handoff.
\n

\n

Required Qualifications &Technical Skills

\n

\n
• 8+ years of hands-on software, data, cloud, platform, or database engineering experience, with strong recent delivery of AI/ML or GenAI applications.
\n
• Strong Python development and REST API integration skills; solid SQL/PLSQL and Oracle Database experience.
\n
• Hands-on experience with LLM applications, RAG, embeddings, vector search, prompt engineering, agent/tool orchestration, and AI evaluation.
\n
• Hands-on data engineering experience across ETL/ELT, document processing, metadata, data quality, APIs, structured/unstructured data, and retrieval pipelines.
\n
• Hands-on OCI experience including IAM, networking, security, databases, logging/monitoring, automation, and cloud-native deployment patterns.
\n
• Practical Terraform, Git, CI/CD, Linux, containers, secrets management, observability, and troubleshooting experience.
\n
• Working knowledge of AI/application security, threat modeling, API security, authorization, encryption, logging/SIEM, and LLM-specific security risks.
\n
• Ability to independently move from requirement and architecture to working code, deployed environment, test evidence, demonstration, and production-transition artifacts.
\n

\n

Preferred Qualifications

\n

\n
• OCI Generative AI / AI Foundations certification and OCI DevOps or Architect certification.
\n
• Oracle Database certification; Autonomous Database/ OCI Data Integration experience.
\n
• OCI Security experience; CISSP, CCSP, or equivalent security certification is advantageous.
\n
• Kubernetes/OKE, workflow/agent frameworks, FinOps/cost optimization, and cloud observability experience.
\n
• U.S. Federal,SLED, DoD, healthcare, financial, or other regulated-data delivery experience.
\n
• Knowledge of NIST/FedRAMP controls, DoD RMF / DISA Impact Levels, Government data handling, governance, and lineage practices.
\n

\n

Key Deliverables

\n

\n
• Working secureRAG and agenticAI accelerators with reusable code/components.
\n
• Agency-specific AI PoVs and demonstrable applications/APIs.
\n
• AI-ready ingestion pipelines, Oracle vector/RAG data layer, and reusable data onboarding components.
\n
• OCI environments, Terraform/IaC modules, CI/CD pipelines, and automated deployment patterns.
\n
• AI security controls, threat models, evaluation/security test suite, findings, and remediation evidence.
\n
• Observability dashboards, technical documentation, deployment packages, and production operations/runbooks.
\n

\n

Performance Measures

\n

\n
• PoV delivery cycle time and percentage of PoVs transitioned toward pilot/production.
\n
• Grounded-answer/retrieval quality, hallucination/refusal behavior, and agent task-completion rate.
\n
• Pipeline reliability, data quality, deployment success rate, and environment provisioning time.
\n
• Security control coverage, defect remediation, unauthorized-access/data-leakage test results, and auditability.
\n
• Platform availability, performance, cost visibility, mean time to recover, automation coverage, and reuse of components.
\n

\n

\n

Education

\n

Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Information Systems, Cybersecurity, or a related discipline. A master’s degree is preferred for lead-level roles.

\n

Public Sector / Security Considerations

\n

The role may support U.S. Federal, state/local, or DoD opportunities. Candidates should be able to work within customer security, data-handling, citizenship, background-investigation, and clearance requirements applicable to the specific engagement. An active security clearance is preferred where relevant but is not required for every Innovation Lab assignment.

\n

Role in the EIT OracleAI Innovation Lab

\n

This role is the hands-on engineering lead across the lifecycle: Agency Requirement → Discovery→ Use-Case Qualification → Technical Design → Build & Integrate → Proof-of-Value → Security & Governance Validation → Agency Demonstration → Production Hardening → Pilot / Production Opportunity.

\n

\n

About ExpediteInfoTech (EIT)

\n

EIT is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by applicable law.