2 hours ago
Ho Chi Minh City, VietnamMid Level / Senior
Responsibilities
- Design, build, and deploy end-to-end Generative AI applications, including LLM-based copilots, chatbots, RAG pipelines, multi-agent systems, and multimodal workflows.
- Develop and optimize RAG solutions using vector databases, embeddings, metadata filtering, and grounding techniques.
- Implement prompt engineering, tool and function calling, and agent orchestration for reliable task execution and workflow automation.
- Integrate GenAI solutions with enterprise systems and data sources through APIs, event-driven architectures, and secure connectors.
- Translate stakeholder requirements into technical architectures and implementation plans in collaboration with product owners and business stakeholders.
- Establish LLMOps practices covering deployment pipelines, monitoring, logging, evaluation, cost optimization, and performance tuning.
- Implement AI safety, governance, and guardrails, including content filtering, grounding validation, access control, and compliance requirements.
- Evaluate emerging GenAI models, frameworks, and tooling to improve quality, latency, and cost efficiency.
- Create reusable libraries, templates, documentation, and reference implementations to build internal GenAI capabilities.
Requirements
- Requires 3–5 years of experience in software engineering, AI engineering, ML engineering, or related fields.
- Requires at least one year of hands-on Generative AI development experience building and deploying LLM-based applications.
- Requires strong proficiency in Python and experience developing APIs and backend services.
- Requires practical experience with LLMs, embeddings, vector databases, and RAG architecture.
- Requires familiarity with GenAI frameworks such as LangChain, Semantic Kernel, LlamaIndex, or similar.
- Requires experience with cloud AI platforms such as Azure, Databricks, or NVIDIA, or equivalent platforms.
- Requires a bachelor’s degree in computer science, computer engineering, information technology, artificial intelligence, or a related discipline.
- Preferred qualifications include a master’s degree or PhD in computer science, AI, electrical engineering, physics, or a related field.
- Preferred qualifications include four or more years of AI, ML, or software engineering experience and production-grade enterprise GenAI delivery experience.
- Preferred qualifications include experience with LLMOps, MLOps, CI/CD, cloud-native architectures, and relevant Generative AI certifications.
Benefits
- Flexible hybrid working arrangement.
- Opportunities to learn and grow while developing the career you want.
- Supportive, inclusive environment focused on employee well-being and development.
- Equal opportunity employment and reasonable accommodation support during the application process.
Categories
