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Sr. Gen AI Engineer

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Job Description - Sr. Gen AI Engineer

Company Description

For More Open Positions Visit us at:

http://recruiting.woongjininc.com/



Our Mission



WOONGJIN, Inc. is a rapidly growing team who provides a range of unique, exceptional, and enhanced services to our clients. We have a strong moral code that includes the service of goodness without expectations of reward. We are motivated by the sense of responsibility and servant leadership.

Benefits

  • Medical Insurance
  • Vision Insurance
  • Dental Insurance
  • 401(k)
  • Paid Sick hours

Job Description

  • Design and develop algorithms for generative models using deep learning techniques
  • Design and build LLM-powered applications for internal and/or customer-facing use cases
  • Develop and productionize RAG pipelines using enterprise data sources, vector databases, and retrieval systems
  • Build and optimize AI agents / agentic workflows for task automation, reasoning, and orchestration
  • Integrate model providers such as OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and open-source models where appropriate
  • Create robust evaluation frameworks for response quality, factuality, latency, safety, and reliability
  • Implement prompt engineering, structured outputs, tool calling, and model optimization strategies
  • Deploy scalable AI services to cloud environments using modern software engineering and MLOps practices
  • Build monitoring, observability, and feedback loops for model and application performance in production
  • Establish and maintain guardrails, responsible AI practices, and security controls for enterprise AI systems
  • Collaborate with product managers, designers, and business stakeholders to identify high-impact AI opportunities
  • Mentor other engineers and contribute to architecture, technical direction, and engineering best practices

Qualifications

Required Qualifications

  • Bachelor’s degree in Computer Science, Engineering, Machine Learning, or a related field
  • 5+ years of software engineering, machine/deep learning engineering, or applied AI experience
  • 2+ years of hands-on experience building and deploying Generative AI / LLM-based systems in production
  • Strong programming skills in Python and experience with backend/API development
  • Experience with LLM application development, including prompt engineering, RAG, tool use, and structured output design
  • Experience in optimizing RAG pipelines using both structured and unstructured data
  • Experience with orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent
  • Experience in generative AI techniques such as GANs, and VAEs
  • Hands-on experience with vector databases / retrieval systems such as Pinecone, Weaviate, Chroma, FAISS, Elasticsearch, or Azure AI Search
  • Experience with cloud platforms such as AWS, GCP, or Azure
  • Experience with Docker, Kubernetes, CI/CD, and production deployment practices
  • Strong understanding of software architecture, scalability, reliability, and distributed systems
  • Experience building evaluation, testing, and monitoring for AI systems
  • Strong communication skills and ability to work closely with technical and non-technical stakeholder

 

Preferred Qualifications

  • Experience fine-tuning or adapting open-source LLMs
  • Advanced knowledge of natural language processing for text generation tasks
  • Experience with PyTorch, TensorFlow, JAX, or related ML frameworks
  • Experience with MLOps tools such as MLflow, SageMaker, Vertex AI, Azure ML, Kubeflow, or similar
  • Experience building multi-agent systems or advanced orchestration workflows
  • Experience with AI safety, guardrails, red-teaming, privacy, and governance
  • Familiarity with search, ranking, recommendation, conversational AI, or enterprise knowledge systems
  • Experience in customer-facing or enterprise SaaS products
  • Experience in semiconductor/manufacturing, retail and e-commerce sectors

 

What Success Looks Like

  • Deliver production-ready GenAI features that improve user experience and business outcomes
  • Build reliable and scalable AI systems with strong quality, latency, and cost performance
  • Establish best practices for evaluation, observability, and responsible AI development
  • Help define the company’s long-term Generative AI architecture and roadma

     

Additional Information

All your information will be kept confidential according to EEO guidelines.

 *** NO C2C ***

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