Role Description The Generative AI Engineer designs, builds, and ships production-grade GenAI systems for our clients and our own platforms. You will work at the intersection of large language models, agent architectures, and cloud engineering on AWS, turning ambiguous problems into reliable, secure, and measurable AI solutions. You will partner closely with delivery teams and clients to move ideas from prototype to production. Key Responsibilities - Design and build LLM-powered applications and agentic systems using Amazon Bedrock, including Bedrock AgentCore and Bedrock Agents. - Architect and implement retrieval-augmented generation (RAG) pipelines, including chunking, embeddings, vector search, and reranking. - Develop prompt strategies, tool and function calling, and orchestration using frameworks such as LangChain or LlamaIndex. - Fine-tune and customize foundation models on Amazon SageMaker when business requirements call for it. - Define and implement evaluation, guardrails, and observability so AI systems are measurable, safe, and cost-aware in production. - Deploy and operate GenAI workloads on AWS using infrastructure-as-code, containers, and CI/CD. - Collaborate with delivery teams and clients to scope solutions, run discovery, and translate requirements into working systems. - Mentor engineers on GenAI patterns and contribute to reusable accelerators and internal platforms. Qualifications - 3+ years building software in production, with 1+ year of hands-on work building LLM or generative AI applications (professional, open-source, or substantial personal projects all count). - Strong Python skills for building AI applications, services, and automation. - Practical experience with LLM application development including prompt engineering, RAG architecture, and function/tool calling. - Hands-on experience with Amazon Bedrock; familiarity with agent runtimes such as Bedrock AgentCore or comparable agent frameworks, and with agent-to-tool standards such as the Model Context Protocol (MCP) or equivalent. - Experience with at least one vector store (OpenSearch, pgvector, Pinecone, Weaviate, or similar) and embeddings. - Comfortable working in AWS day to day, including IAM, Lambda, S3, and basic networking concepts. - Experience building and consuming REST APIs and integrating LLMs into existing systems, including authentication, asynchronous patterns, streaming, and error and timeout handling. - Sound judgment about handling sensitive and client data with AI systems, including PII, what may and may not be sent to a model, and basic data governance and compliance. - Able to work directly with clients and delivery teams to scope problems, run working sessions, and set realistic expectations about what generative AI can and cannot do. - Experience with Git-based workflows, containers (Docker), and shipping code through CI/CD. - Understanding of how to evaluate, guardrail, and monitor LLM systems for accuracy, safety, and cost. Extra Credit - Fine-tuning techniques such as LoRA, QLoRA, or PEFT, and model customization on SageMaker. - Machine learning fundamentals, feature engineering, and the model experimentation lifecycle. - Python ML and data libraries such as PyTorch, Hugging Face Transformers, scikit-learn, pandas, and NumPy. - MLOps tooling such as SageMaker Pipelines, Model Registry, or Feature Store. - Responsible AI practices including bias evaluation and explainability (for example, SageMaker Clarify). - Data engineering exposure such as SQL, AWS Glue, Athena, or Spark for preparing data that feeds AI systems. Core Competencies - Action Oriented – Takes on new opportunities and tough challenges with urgency, energy, and enthusiasm. - Tech-Savvy – Anticipates the impact of emerging AI technologies and adapts quickly as the landscape shifts. - Collaborates – Builds partnerships and works effectively with delivery teams and clients to meet shared objectives. - Communicates Effectively – Translates complex technical concepts for both technical and non-technical audiences. - Cultivates Innovation – Creates new and better ways for the organization and its clients to succeed. - Adaptable – Operates effectively in unfamiliar and imperfect client environments, inheriting existing stacks, accounts, and constraints rather than building from a clean slate. - Plans and Aligns – Scopes effort, estimates realistically, and makes sound tradeoffs to deliver on time and within budget on billable client work. Benefits - Flexible working hours and remote work environments. - Robust benefits package including medical, dental, vision, life insurance, disability insurance, and 401k matching. Additional Information Artisan is an equal opportunity employer and does not unlawfully discriminate on the basis of race, color, religion, gender, sexual orientation, sex, gender identity, gender expression, national origin, ancestry, age, medical condition, disability, veteran status, marital status or other prohibited factors under applicable federal, state, and local laws. This position requires constant operation of a computer, various software programs and other electronic productivity tools. This position requires prolonged observation of a computer screen. Additionally, it requires regular participation in virtual meetings and training. Artisan participates in e-Verify to confirm the employment eligibility of all new hires as part of our commitment to a compliant and secure workplace.