AI Solutions Engineer
Lexical Intelligence
Lexical Intelligence provides software and services related to processing large-scale biomedical information sources. Our NLP and analytics software is used by policy and decision makers to evaluate and prioritize current and emerging areas of research.
We are looking for an Artificial Intelligence (AI) Solutions Engineer with analytical experience to work for a federal client in Bethesda, MD. The AI Solutions Engineer will design, develop, and operationalize AI-driven capabilities that enable advanced automation and decision support across a range of applications. This role focuses on piloting and implementing AI solutions, including standing up lightweight applications and applets within a shared infrastructure environment, integrating foundation models, and translating emerging AI techniques into practical tools. The engineer will work across the full lifecycle of AI systems, supporting data preparation, model integration, evaluation, deployment, and monitoring, while collaborating with cross-functional teams to ensure solutions are scalable, reliable, and aligned with mission needs. Experience developing and deploying AI or machine learning-based applications, as well as working with large and complex datasets and modern AI tooling, is required. The AI Solutions Engineer shall be process oriented, and able to work well within a team of analysts, data scientists, and software developers. Applicants selected will be subject to a pre-employment background and reference check. Qualifications Minimum qualifications: Five to seven years of relevant experience with the items below:- Strong programming and AI/ML development skills, including Python, experience with modern frameworks, and with integrating AI models into existing software architectures
- Experience designing and deploying AI solutions end-to-end, including integrating models into applications or shared infrastructure
- Strong background in algorithm design and data structures; comfort with software testing, debugging, and system architecture
- Solid data analysis and data engineering capability, including working with large datasets and translating findings into usable outputs
- Proficiency with cloud-based and containerized environments (e.g., AWS, Kubernetes) to support scalable AI workloads
- Ability to evaluate, iterate, and operationalize AI solutions, including performance measurement, experimentation, and collaboration with cross-functional teams
- Bachelor's degree in Computer Science, Software Engineering, Data Science or related quantitative area
- Ability to obtain a Public Trust Clearance
- Ph.D. or advanced Master's degree in a relevant scientific or quantitative discipline
- Experience supporting NIH or other federal biomedical research organizations
- Experience applying large language models (LLMs) to scientific analysis, information extraction, summarization, or question answering
- Experience with Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search
- Experience evaluating AI systems using benchmark-driven and human-in-the-loop methodologies
- Experience analyzing publications, grants, patents, clinical trials, or other large-scale scientific datasets
- Familiarity with MLOps practices such as model versioning, monitoring, and lifecycle management in production environments
- Experience building AI prototypes or pilot applications in shared or enterprise environments
- Knowledge of data governance, security, and compliance considerations, particularly in regulated or federal environments
- Exposure to modern data platforms and distributed processing frameworks (e.g., Spark, Databricks)
- Strong communication skills with the ability to explain AI concepts and analytical findings to non-technical stakeholders
- Experience working in cross-functional or product-oriented teams to translate requirements into technical solutions
- Strong scientific reasoning and critical thinking
- Ability to interpret complex scientific literature
- Curiosity for emerging AI technologies and biomedical research
- Commitment to reproducible, transparent, and high-quality analytical methods
Vacancy posted 2 days ago
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