Senior Generative AI Engineer
근무지: Ridgefield Park, NJ (On-site)
형태: 1년 계약직
급여: $10,950/월

LLM 기반 AI 애플리케이션 설계·개발·배포를 리딩하는 시니어 Generative AI 엔지니어 포지션입니다.

주요 업무

LLM 기반 AI 애플리케이션 및 RAG 시스템 설계/개발
AI Agent 및 자동화 워크플로우 구축
OpenAI, Anthropic, Azure, AWS 등 모델 연동
프롬프트 엔지니어링 및 모델 최적화
AI 시스템 배포 및 운영 (MLOps, 클라우드)
성능 평가, 모니터링 및 품질 개선
비즈니스 요구사항을 AI 솔루션으로 전환
기술 리딩 및 팀 멘토링

자격 요건

관련 전공 학사 이상
AI/ML 또는 소프트웨어 개발 경력 5년 이상
Generative AI/LLM 실무 경험 2년 이상
Python 및 API 개발 역량
RAG, 벡터DB, LLM 애플리케이션 경험
클라우드(AWS/GCP/Azure) 및 배포 경험

LangChain, LlamaIndex 등 프레임워크 경험
PyTorch/TensorFlow 등 ML 프레임워크
MLOps 및 멀티 에이전트 시스템 경험
AI 보안/거버넌스 이해
SaaS 또는 산업 도메인 경험

📩 지원: Jenniferk@sbtgus.com

Senior Generative AI Engineer
Ridgefield Park, NJ, USA
On-Site
1yr contract
$10950/month

We are seeking a Senior Generative AI Engineer to design, build, and deploy production-grade AI applications powered by large language models (LLMs). In this role, you will lead the end-to-end development of Generative AI solutions, including LLM-powered applications, retrieval-augmented generation (RAG) systems, agentic workflows, model evaluation pipelines, and production infrastructure.

You will work cross-functionally with product, finance, data, and business stakeholders to translate real-world business problems into scalable AI systems that deliver measurable value.

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

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

please send your resume to —————— Jenniferk@sbtgus.com ———————–