Oracle AI & OCI Engineer​/Developer

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 Dev Sec Ops /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.

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

• 8+ years of hands-on software, data, cloud, platform, or database engineering experience, with strong recent delivery of AI/ML or GenAI applications.
• Strong Python development and REST API integration skills; solid SQL/PLSQL and Oracle…