Design, develop, troubleshoot and debug software programs for databases, applications, tools, networks etc. As a Principal Applications Engineer, you should be proficient in writing performant code in Java, building Agentic solutions, building distributed systems, containerization, and cloud-native architectures. You will collaborate with cross-functional teams to build scalable, reliable, and production-ready solutions. Oracle is leading development of Agentic solutions, having a good understanding and experience in leveraging the capabilities of Large Language Models would is a must have.
In this role, you will design, develop, and build scalable services that leverage from latest and greatest advancements in software development and GenAI to solve complex business problems intelligently.
Your expertise will be pivotal in building end to end software solutions flows that includes data base design, conversational AI solutions, Agentic flows using LLM and other cutting-edge
Machine learning techniques to shape up and enhance users' experience. You will also partner with product managers, software engineers, and operation teams to leverage engineering innovations to simplify the business requirements into scalable solutions.
The ideal candidate is highly technical, proficient in Java, data base designs and have experience of building scalable solutions, can lead across the full stack, along with good product sense and business understanding, to map the technology choices to the context of each initiative with a min of 8 years of development experience.
Required Skills
Java & Microservices Architecture
Strong experience building Java-based microservices using frameworks such as Spring Boot or Helidon, with expertise in RESTful API design, containerization (Docker/Kubernetes), and distributed systems patterns.
LLM Integration & Agentic Workflows
Hands-on experience integrating LLM APIs and designing agent-based architectures, including tool and function calling, multi-step reasoning workflows, and state management for orchestrating AI agents.
RAG Pipeline Development
Proven ability to build retrieval-augmented generation (RAG) solutions, including embedding generation, vector databases, semantic and hybrid search, and effective document chunking strategies.
AI Guardrails, Trust & Safety
Experience implementing safeguards such as input and output validation, PII detection and redaction, prompt injection prevention, rate limiting, content moderation, and hallucination mitigation.
Vector Search & Databases
Proficiency with vector databases (e.g., Oracle AI Vector Search, Pinecone, or similar), embedding optimization, semantic similarity search, and relational databases such as Oracle or PostgreSQL.
Backend & API Development
Strong backend development skills, including API authentication and authorization (OAuth 2.0, JWT), request validation, error handling, documentation, and enterprise integration patterns like event-driven and message-based architectures.
Document Processing & Ingestion
Experience extracting and processing content from multiple formats (PDF, DOCX, PPT), including document parsing, metadata extraction, and preparing structured and unstructured data for AI pipelines.
Cloud & DevOps
Familiarity with cloud platforms (OCI preferred; AWS, Azure, or GCP acceptable), CI/CD pipelines, automated testing, infrastructure as code, and monitoring, logging, and observability practices.
Testing & Quality Engineering
Strong testing discipline across traditional and AI-powered systems, including unit and integration testing, prompt and output validation, performance testing, and adherence to code quality standards.
Communication & Problem Solving
Ability to clearly explain complex AI concepts, collaborate across technical and non-technical teams, troubleshoot distributed systems, and creatively solve GenAI-specific challenges such as context management, reliability, and performance optimization.