Senior Generative AI Engineer

SBT Global•Ridgefield Park, NJ

2d•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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