Gen AI Engineer
PDI Technologies
Role Overview:
We are seeking a highly skilled Generative AI Engineer with strong Quality Engineering expertise to design, build, and validate intelligent AI-driven platform capabilities. This role focuses on integrating advanced Generative AI solutions such as LLMs, Retrieval-Augmented Generation (RAG), and vector-based systemsinto scalable platform services that enable automation, contextual insights, and enhanced decision-making. The ideal candidate will combine deep AI engineering knowledge with robust QA automation practices to ensure the reliability, accuracy, and safety of AI-powered features. You will play a key role in developing evaluation frameworks, implementing automated testing strategies, and integrating continuous testing within CI/CD pipelines, while collaborating cross-functionally to deliver high-performance, innovative, and trustworthy AI solutions.
Key Responsibilities
- AI Platform Architecture & Implementation: Design, build, and deploy intelligent AI-driven platform capabilities that enable automation, intelligent insights, and advanced decision-making across distributed services and data systems.
- Product Innovation: Drive the development of next-generation AI-enabled platform features that enhance operational efficiency, enable intelligent automation, and deliver scalable, high-performance digital experiences.
- Generative AI Integration: Seamlessly integrate generative AI capabilities into platform services, including prompt engineering, Retrieval-Augmented Generation (RAG), intelligent workflows, and contextual reasoning systems.
- Intelligent System Leverage: Utilize Large Language Models (LLMs), embeddings, and vector-based retrieval systems to build intelligent, context-aware solutions that enhance platform capabilities and automation.
- AI Quality & Evaluation: Develop frameworks and methodologies for validating AI system performance, including response accuracy, model reliability, safety evaluation, and output consistency.
- AI Testing & Validation: Design and implement automated validation strategies to ensure reliability and correctness of AI-driven features, including automated test generation, model evaluation, and workflow validation.
- QA Automation Engineering: Develop and maintain scalable automation frameworks for validating platform services, APIs, and AI-enabled capabilities using modern testing tools and frameworks.
- API & Integration Testing: Build automated test suites to validate API functionality, service integrations, and distributed system interactions to ensure reliability and performance.
- Continuous Testing & CI/CD Integration: Integrate automated testing into CI/CD pipelines to enable continuous testing, faster feedback loops, and high-quality releases.
- Cross-Functional Collaboration: Work closely with engineering, product, data, and platform teams to translate complex requirements into scalable AI-enabled solutions while maintaining high-quality engineering standards.
- Technical Leadership: Provide guidance on AI engineering practices, automation strategies, and quality engineering standards to support platform reliability and intelligent system development.
- Mission Alignment: Contribute to building innovative, reliable, and ethically developed AI-powered
Qualifications
- 8+ years of experience in software engineering, quality engineering, or platform engineering, including experience building automation frameworks and AI-enabled systems.
- Generative AI & LLM Integration: Hands-on experience integrating Large Language Models (LLMs) into applications, including prompt engineering, RAG architectures, AI workflow orchestration, and intelligent automation systems.
- Automation Engineering Expertise: Strong experience designing and implementing test automation frameworks using tools such as Selenium, Cypress, TestNG, JUnit, or similar technologies.
- API Testing & Integration: Experience building automated tests for RESTful APIs and microservices using tools such as Postman, REST Assured, or similar frameworks.
- Programming Proficiency: Strong programming skills in at least two of the following languages: Python, Java, JavaScript, or C#.
- Cloud Platform Experience: Experience building and deploying applications on major cloud platforms such as AWS, GCP, or Azure, including containerized environments and CI/CD pipelines.
- Data & AI Technologies: Familiarity with vector databases, embeddings, AI model evaluation techniques, and data pipelines that support intelligent applications.
- System Reliability & Quality: Experience ensuring system reliability through automated testing, continuous integration, performance validation, and platform monitoring.
- DevOps & CI/CD: Experience integrating automated testing and validation frameworks within CI/CD pipelines for scalable platform delivery.
- Candidates must demonstrate the ability to effectively leverage AI tools, automation, and modern AI technologies to enhance productivity, problem-solving, and innovation in their daily work. Proven ability to use AI-powered tools to improve efficiency, accelerate development, and solve complex problems. AI proficiency is required: ability to use and adapt to modern AI tools and technologies as part of day-to-day work.
- Strong communication and collaboration skills with the ability to work across engineering, product, and platform teams to deliver intelligent, production-ready solutions.
Behavioral Competencies
- Ensures Accountability
- Manages Complexity
- Communicates Effectively
- Balances Stakeholders
- Collaborates Effectively