Job Summary
Job Summary – Senior Generative AI Engineer / AI Solutions Developer
We are seeking an experienced Generative AI Engineer with expertise in Vertex AI, Gemini Models, LangChain, LlamaIndex, Prompt Engineering, Vector Search, Agentic AI, and AI Evaluation Frameworks. The ideal candidate will be responsible for designing, developing, and deploying enterprise-grade AI solutions that leverage Large Language Models (LLMs), retrieval systems, and intelligent autonomous agents to solve complex business challenges.
The role involves building scalable AI applications, implementing Retrieval-Augmented Generation (RAG) architectures, developing multi-agent workflows, optimizing prompts, and establishing robust AI evaluation and monitoring frameworks to ensure quality, accuracy, and business value.
Key Responsibilities
Key Responsibilities
- Design, develop, and deploy Generative AI applications using Vertex AI and Gemini Models.
- Build scalable RAG (Retrieval-Augmented Generation) solutions using LangChain and LlamaIndex.
- Design and optimize prompts to improve AI model performance, accuracy, and user experience.
- Implement Vector Search solutions using vector databases for semantic search and knowledge retrieval.
- Develop and manage Agentic AI systems, including autonomous and multi-agent workflows.
- Integrate AI services with enterprise applications, APIs, databases, and cloud-native platforms.
- Establish AI testing, evaluation, and monitoring processes to measure model quality, relevance, safety, and performance.
- Collaborate with product owners, architects, and engineering teams to translate business requirements into AI-driven solutions.
- Optimize AI applications for scalability, security, reliability, and cost efficiency.
- Stay current with emerging trends in LLMs, agent frameworks, AI governance, and responsible AI practices.
Skill Requirements
Required Skills
- Strong experience with Google Vertex AI and Gemini Models.
- Hands-on expertise in LangChain and LlamaIndex frameworks.
- Advanced knowledge of Prompt Engineering techniques and LLM optimization.
- Experience implementing Vector Search, embedding models, and semantic retrieval systems.
- Strong understanding of RAG architectures, knowledge retrieval, and contextual AI systems.
- Experience designing and developing Agentic AI and multi-agent applications.
- Expertise with AI Evaluation Frameworks, model testing, benchmarking, and observability.
- Proficiency in Python and API-based integrations.
- Strong understanding of cloud-native development and software engineering best practices.
- Excellent analytical, problem-solving, and stakeholder communication skills.