Oracle
Principal AI Application Engineer
Job Description
Qualifications and Technical Expertise:
- Qualification & Experience: Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field. Minimum 3+ years’ hands-on experience in software development using Python, Database with strong grounding in data structures, algorithms, and their application in solving complex engineering challenges.
- Application Development: Experience in designing, scalable microservices in an enterprise environment.
- Self-Sufficiency: Proven ability to rapidly learn new technologies, prototype solutions, and independently design and implement application components.
- LLM Technologies: Practical exposure to working with Large Language Models (OpenAI, Grok, or open-source variants), including prompt engineering practices, fine-tuning methods, and model deployment strategies.
- Agentic Frameworks: Hands-on development of agent-based workflows using frameworks such as OpenAI Agent SDK, LangGraph, or equivalent agent orchestration toolsets.
- RAG Systems: Experience implementing Retrieval-Augmented Generation components including indexing, metadata strategies, hybrid search, relevance evaluation, and pipeline integration.
- OCI Services: Experience with Oracle Cloud Infrastructure, including services such as OCI GenAI Service, Object Storage, API Gateway, Functions, or Streaming.
- Containers: Hands-on familiarity with Kubernetes on Oracle Kubernetes Engine (OKE) and container tooling such as Podman or Docker.
Preferred Qualifications:
- OCI Services: Experience with Oracle Cloud Infrastructure, including services such as OCI GenAI Service, Object Storage, API Gateway, Functions, or Streaming.
- Containers: Hands-on familiarity with Kubernetes on Oracle Kubernetes Engine (OKE) and container tooling such as Podman or Docker.
- Vector Data Platforms: Familiarity with vector-enabled data systems such as Oracle 23ai Vector Database, Pinecone, FAISS, or comparable technologies (desirable).
Soft Skills & Leadership
- Proven ability to drive technical outcomes, take ownership of deliverables, and work independently in fast-evolving AI solution spaces.
- Strong communication skills, with the ability to articulate technical concepts, document solution approaches, and collaborate across distributed teams.
- Demonstrated problem-solving ability when working with complex AI workloads, distributed systems, and cloud-native application behaviours.
- A proactive, experimentation-oriented mindset with a strong willingness to learn emerging AI technologies, frameworks, and engineering patterns.
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