Concepts Beyond
Principal AI Engineer & Data Analyst
Concepts Beyond, Baltimore, Maryland, United States, 21276
Base pay range
$120,000.00/yr - $160,000.00/yr
Overview Shape Safer Skies with Data! This challenging and rewarding role combines strategic vision, analytical leadership, and hands‑on technical expertise to lead data‑driven Artificial Intelligence (AI) innovation initiatives. You will define and implement enterprise AI and data strategy, architect scalable analytical systems, and deliver AI/ML analytics to improve aviation. Work with stakeholders, engineers, and data scientists to analyze the results within the context of the aviation domain and transform data into executable information that can impact business decisions and policies.
Concepts Beyond provides Systems Engineering and aviation expertise to advance Air Traffic Management concepts and develop Information Management & related technologies including AI, Cloud Computing, Mobile Apps, and Cybersecurity.
Essential Functions Strategic Leadership
Develop and execute Data, Analytics, and AI strategy, aligning with customer requirements and business goals.
Lead the technical direction of AI / Machine Learning (ML) research & development and create AI/ML adoption frameworks and governance models for scalable enterprise deployment.
Identify opportunities for AI enablement, automation, and advanced analytics across business functions.
Represent the company’s AI/ML portfolio through thought leadership; engaging customers, industry, and partners; contribute to technical publications, author white papers, and speaking at live events.
Hands-On AI/ML Development & Data Analysis
Define aviation safety data requirements, methodologies, and AI/ML models to generate actionable safety metrics.
Analyze complex datasets in the aviation domain and translate insights into strategic recommendations.
Architect and implement scalable data pipelines, feature stores, and model workflows using Airflow, Prefect, AWS Glue, or Azure Data Factory on AWS, Azure, or GCP.
Build, train, and deploy AI/ML models for Natural Language Processing (NLP), computer vision, predictive analytics, and generative AI, including Large Language Models (LLMs) and VectorRAG/GraphRAG/HybridRAG frameworks.
Develop and validate data transformations, normalization, and anomaly detection to ensure quality and integrity.
Use distributed frameworks such as Spark, Flink to process and integrate large‑scale data with enterprise platforms (Palantir Foundry, AWS, Azure, GCP).
Establish and enforce data governance for lineage, transformation logic, privacy, and ethical AI compliance.
Deploy AI agents and vector‑based retrieval systems to enhance automation and decision‑making.
Collaborate with data scientists, analysts and engineers to ensure model scalability, performance, and reliability.
Required Qualifications
Bachelor’s or Master’s degree in Engineering or related technical field. Advanced degrees such as Ph.D. can be substituted for some experience.
5+ years of AI, data engineering experience designing and maintaining scalable data pipelines and analytics systems.
Data modeling expertise with normalization, star/snowflake schemas, and Type 1/Type 2 slowly changing dimensions.
Experience developing, deploying, and maintaining machine learning models for classification, anomaly detection, and NLP techniques for text analysis, information extraction, and sentiment / entity extraction.
Hands‑on experience with AI/ML data pipelines and preprocessing for LLMs, including data extraction, chunking, embedding, and grounding, and Generative AI and Retrieval‑Augmented Generation (RAG) architectures.
Proficient in Python with strong software engineering practices: object‑oriented design, testing frameworks (pytest, unittest), logging, error handling, and version control.
Expertise with big data process frameworks (Hadoop, Spark, Flink, etc.), relational databases (PostgreSQL, MySQL, SQL Server) and non‑relational databases (Elasticsearch, MongoDB, etc.), ETL/ELT orchestration using Apache Airflow, Prefect, Luigi, AWS Glue, or Azure Data Factory.
Proficient with cloud data ecosystems (AWS, Azure, GCP) and data integration within enterprise platforms.
Desired Skills
Aviation Safety domain or FAA/NAS systems exposure (highly desirable)
Palantir Foundry platform for data integration and AI model deployment (highly desirable)
LLM and RAG pipelines using vector databases such as Pinecone, Weaviate, ChromaDB, FAISS
Speech technologies: STT/TTS systems such as Whisper, Google Cloud Speech, Azure Speech Services, custom voice models, accent adaptation
Streaming NLP, real‑time inference, AI agents/agentic architectures
LLM fine‑tuning using PyTorch, TensorFlow, Hugging Face Transformers (LoRA, QLoRA)
DevSecOps in regulated or safety‑critical environments, and NPEIDMS
Data visualization tools (Tableau, Power BI, Looker)
API integrations (RESTful/GraphQL) / API-TRAX and streaming platforms such as Kafka, Kinesis, Pulsar
Containerization (Docker, Kubernetes) and infrastructure‑as‑code (Terraform, CloudFormation)
Data serialization formats and tradeoffs (Parquet, Avro, ORC, JSON, CSV, BIN‑HEX)
Seniority level Associate
Employment type Part‑time
Job function Engineering, Analyst, and Research
Industries Airlines and Aviation and IT Services and IT Consulting
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Overview Shape Safer Skies with Data! This challenging and rewarding role combines strategic vision, analytical leadership, and hands‑on technical expertise to lead data‑driven Artificial Intelligence (AI) innovation initiatives. You will define and implement enterprise AI and data strategy, architect scalable analytical systems, and deliver AI/ML analytics to improve aviation. Work with stakeholders, engineers, and data scientists to analyze the results within the context of the aviation domain and transform data into executable information that can impact business decisions and policies.
Concepts Beyond provides Systems Engineering and aviation expertise to advance Air Traffic Management concepts and develop Information Management & related technologies including AI, Cloud Computing, Mobile Apps, and Cybersecurity.
Essential Functions Strategic Leadership
Develop and execute Data, Analytics, and AI strategy, aligning with customer requirements and business goals.
Lead the technical direction of AI / Machine Learning (ML) research & development and create AI/ML adoption frameworks and governance models for scalable enterprise deployment.
Identify opportunities for AI enablement, automation, and advanced analytics across business functions.
Represent the company’s AI/ML portfolio through thought leadership; engaging customers, industry, and partners; contribute to technical publications, author white papers, and speaking at live events.
Hands-On AI/ML Development & Data Analysis
Define aviation safety data requirements, methodologies, and AI/ML models to generate actionable safety metrics.
Analyze complex datasets in the aviation domain and translate insights into strategic recommendations.
Architect and implement scalable data pipelines, feature stores, and model workflows using Airflow, Prefect, AWS Glue, or Azure Data Factory on AWS, Azure, or GCP.
Build, train, and deploy AI/ML models for Natural Language Processing (NLP), computer vision, predictive analytics, and generative AI, including Large Language Models (LLMs) and VectorRAG/GraphRAG/HybridRAG frameworks.
Develop and validate data transformations, normalization, and anomaly detection to ensure quality and integrity.
Use distributed frameworks such as Spark, Flink to process and integrate large‑scale data with enterprise platforms (Palantir Foundry, AWS, Azure, GCP).
Establish and enforce data governance for lineage, transformation logic, privacy, and ethical AI compliance.
Deploy AI agents and vector‑based retrieval systems to enhance automation and decision‑making.
Collaborate with data scientists, analysts and engineers to ensure model scalability, performance, and reliability.
Required Qualifications
Bachelor’s or Master’s degree in Engineering or related technical field. Advanced degrees such as Ph.D. can be substituted for some experience.
5+ years of AI, data engineering experience designing and maintaining scalable data pipelines and analytics systems.
Data modeling expertise with normalization, star/snowflake schemas, and Type 1/Type 2 slowly changing dimensions.
Experience developing, deploying, and maintaining machine learning models for classification, anomaly detection, and NLP techniques for text analysis, information extraction, and sentiment / entity extraction.
Hands‑on experience with AI/ML data pipelines and preprocessing for LLMs, including data extraction, chunking, embedding, and grounding, and Generative AI and Retrieval‑Augmented Generation (RAG) architectures.
Proficient in Python with strong software engineering practices: object‑oriented design, testing frameworks (pytest, unittest), logging, error handling, and version control.
Expertise with big data process frameworks (Hadoop, Spark, Flink, etc.), relational databases (PostgreSQL, MySQL, SQL Server) and non‑relational databases (Elasticsearch, MongoDB, etc.), ETL/ELT orchestration using Apache Airflow, Prefect, Luigi, AWS Glue, or Azure Data Factory.
Proficient with cloud data ecosystems (AWS, Azure, GCP) and data integration within enterprise platforms.
Desired Skills
Aviation Safety domain or FAA/NAS systems exposure (highly desirable)
Palantir Foundry platform for data integration and AI model deployment (highly desirable)
LLM and RAG pipelines using vector databases such as Pinecone, Weaviate, ChromaDB, FAISS
Speech technologies: STT/TTS systems such as Whisper, Google Cloud Speech, Azure Speech Services, custom voice models, accent adaptation
Streaming NLP, real‑time inference, AI agents/agentic architectures
LLM fine‑tuning using PyTorch, TensorFlow, Hugging Face Transformers (LoRA, QLoRA)
DevSecOps in regulated or safety‑critical environments, and NPEIDMS
Data visualization tools (Tableau, Power BI, Looker)
API integrations (RESTful/GraphQL) / API-TRAX and streaming platforms such as Kafka, Kinesis, Pulsar
Containerization (Docker, Kubernetes) and infrastructure‑as‑code (Terraform, CloudFormation)
Data serialization formats and tradeoffs (Parquet, Avro, ORC, JSON, CSV, BIN‑HEX)
Seniority level Associate
Employment type Part‑time
Job function Engineering, Analyst, and Research
Industries Airlines and Aviation and IT Services and IT Consulting
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