The
Data Engineer will lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption. You will design and operationalize modern data and retrieval foundations to support LLM-powered applications including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data. You will be partnering with a team working with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring. This role blends hands-on technical ability with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments.
Requirements - TS/SCI FSP Clearance
- Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field
- 6+ years of consulting experience leading delivery teams, including onshore and offshore team members
- 6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
- 5+ years of experience working in an AI environment
- 5+ years of experience translating requirements into client ready design documents
- 5+ years of experience in software application architecture analysis, design, and delivery
- 5+ years of experience executing full system development life cycle implementations
Desired Skills - Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
- 5 + years of experience in Data Science, Statistics, and Machine Learning
- 5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
- 5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
- 5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure
