Generative AI Engineer

Be an Early Applicant Remote Hiring Remotely in USA 6-6 Annually Senior level Remote Hiring Remotely in USA 6-6 Annually Senior level The Generative AI Engineer will research, design, and develop AI/ML solutions, focusing on large language models and cloud-native services. The summary above was generated by AI

NextGen Federal Systems, LLC. is seeking a Generative AI/Machine Learning Engineer to research, design, develop, and deploy innovative AI/ML and Generative AI solutions across a variety of mission-focused problem sets. The selected candidate will support the development of advanced AI capabilities using technologies such as large language models, retrieval-augmented generation, Model Context Protocol servers, agentic workflows, and cloud-native AI services including AWS Bedrock.

The selected candidate will be part of a distributed development team operating in a dynamic, agile, fast-paced environment and will participate in all phases of the software engineering lifecycle, including research, requirements analysis, solution design, model development, integration, deployment, evaluation, and testing.

• 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, · 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