Generative AI Engineer

Qualifications:

• Bachelor's [Master's / PhD] Degree in Computer Science, Math, Engineering, or related field

• 6+ [4+ / 2+] years of work-related experience with applied machine learning, data science, software engineering, or AI/ML system development

• Experience designing, developing, or deploying machine learning, Generative AI, or data-driven software solutions

Required Skills and Experience:

· Work as part of a technical team to design, develop, implement, and transition AI/ML and Generative AI capabilities that meet client operational requirements

· Experience developing solutions using Python and common AI/ML or data science libraries such as pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, LangChain, LlamaIndex, or similar frameworks

· Familiarity with large language models and Generative AI concepts, including prompt engineering, embeddings, vector databases, retrieval-augmented generation, model evaluation, and responsible AI considerations

· Experience designing or integrating LLM-based applications, including chat-based interfaces, document question-answering systems, workflow automation, summarization tools, or AI-enabled decision-support systems

· Experience using cloud services to build, deploy, or manage AI/ML solutions

· Understanding of machine learning concepts, including data preprocessing, feature engineering, model training, model evaluation, performance metrics, and model deployment best practices

· Strong written and verbal communication skills, including the ability to explain technical concepts to both technical and non-technical stakeholders

· Proficiency with Microsoft Office tools, including Word, Excel, PowerPoint, and Outlook

Desired Skills and Experience:

• Experience working in an Agile development lifecycle

• Experience developing Generative AI solutions using AWS Bedrock, including foundation models, Knowledge Bases, Agents, Guardrails, or related AWS AI/ML services

• Familiarity with Model Context Protocol and the development or integration of MCP servers, tools, resources, or agent-accessible services

• Experience with retrieval-augmented generation architectures, including document ingestion, chunking strategies, embedding models, vector databases, metadata filtering, reranking, and response evaluation

• Experience with agentic AI workflows, tool-calling, function-calling, multi-step reasoning workflows, or orchestration frameworks

• Experience evaluating LLM or RAG-based systems using qualitative and quantitative methods, such as human evaluation, automated LLM-as-judge approaches, RAGAS-style metrics, hallucination analysis, or task-specific performance measures

• Experience deploying AI/ML solutions using cloud-native and DevOps technologies such as AWS, Docker, Kubernetes, CI/CD pipelines, Terraform, or similar tools

• Experience with configuration management and version control technologies such as Git, GitLab, GitHub, or Bitbucket

• History of academic publications, conference presentations, technical reports, demos, or client-facing briefings