AI Engineer

• Design, develop, test, and maintain scalable software applications and AI-enabled solutions.

• Develop backend applications, RESTful APIs, microservices, and distributed services using Java, Spring Boot, and modern software engineering frameworks.

• Build and maintain frontend applications using React.js and modern web development technologies when required.

• Design and implement production-ready LLM-powered applications and AI-native capabilities that address real business use cases.

• Build AI agents and agentic workflows capable of interacting with enterprise APIs, databases, documents, operational systems, and external tools.

• Develop agentic solutions using frameworks and platforms such as Google Agent Development Kit (ADK), LangChain, LangGraph, CrewAI, Semantic Kernel, or similar technologies.

• Implement Model Context Protocol (MCP) integrations, including MCP servers, clients, tools, and reusable enterprise capabilities for AI applications.

• Design and implement Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, semantic search, metadata filtering, document retrieval, reranking, and grounded response generation.

• Integrate LLMs and AI models from platforms such as Google Gemini, OpenAI, Anthropic Claude, or similar enterprise AI platforms.

• Develop secure tool-calling and function-calling workflows that allow AI agents to interact with internal services and enterprise systems.

• Apply prompt engineering and context engineering techniques to improve the accuracy, relevance, and reliability of LLM-based applications.

• Implement AI guardrails, validation mechanisms, structured outputs, grounding techniques, and safety controls to reduce hallucinations and improve application reliability.

• Develop evaluation frameworks for AI applications, including response-quality evaluation, regression testing, hallucination detection, groundedness checks, latency measurement, and cost monitoring.

• Implement AI observability and monitoring capabilities to track model behavior, application performance, token consumption, failures, and production quality.

• Design and implement database solutions using technologies such as PostgreSQL, MongoDB, Apache Druid, and Google BigQuery.

• Integrate applications with messaging and event-streaming technologies such as Apache Kafka.

• Develop cloud-native applications and services designed for scalability, resilience, observability, and high availability.

• Create automated unit, integration, regression, API, and end-to-end tests for software and AI-enabled features.

• Participate in code reviews and follow secure coding standards, engineering best practices, and established software development guidelines.

• Troubleshoot application and AI-system issues, perform root-cause analysis, and implement sustainable solutions.

• Participate in technical design discussions, architecture reviews, and system-design sessions.

• Collaborate with Product, Architecture, Data Engineering, Security, Infrastructure, and Engineering teams throughout the software development lifecycle.

• Use AI-assisted software development tools such as GitHub Copilot, Cursor, Windsurf, ChatGPT Enterprise, Gemini, Claude, or similar tools to improve development productivity, testing, documentation, and code quality.

• Participate in Agile development practices including sprint planning, backlog refinement, estimation, daily stand-ups, sprint reviews, and retrospectives.

• Contribute to engineering standards, reusable components, technical documentation, and continuous improvement initiatives.

Required Experience

• 3+ years of professional software engineering experience developing production applications.

• Experience working across the Software Development Life Cycle (SDLC), including design, development, testing, deployment, and production support.

• Strong programming experience with Java and Spring Boot or comparable backend technologies.

• Experience developing REST APIs, microservices, and distributed applications.

• Experience with modern frontend technologies such as React.js, JavaScript, or TypeScript is preferred.

• Experience working with relational and/or NoSQL databases such as PostgreSQL, MongoDB, BigQuery, or similar technologies.

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