Principal AI Engineer & Data Analyst

Must be US Citizen.

Shape Safer Skies with Artificial Intelligence! Concepts Beyond is seeking a hands-on AI / Data Engineer & Analyst to serve as our AI technical lead, powering aviation safety and air traffic management programs through enterprise-scale data engineering, applied machine learning, and intelligent system design. This is a high-ownership, individual-contributor role — you will personally architect pipelines, train and fine-tune models on mission-critical corpora, and deploy analytics solutions that drive real decisions in the National Airspace System. We welcome candidates from a range of backgrounds, including recent Ph.D. graduates, postdoctoral researchers, government or military technologists moving into industry, and engineers from academic or startup environments — sharp, eager, fast-learning technologists ready to grow into a highly visible, public-facing technical leadership role.

You will design and maintain scalable data infrastructure, build and operationalize AI/ML models, and integrate intelligent capabilities across enterprise aviation platforms. Concepts Beyond builds solutions to improve advance Air Traffic Management, Information Management, cybersecurity, AI, data analytics, safety.

• Essential Functions
• AI, Machine Learning & Intelligent Systems
• Develop, train, and operationalize NLP/ML models for low-latency real-time voice pipelines using streaming speech-to-text and text-to-speech, diarization, classification, and named entity recognition over controller–pilot voice and text.
• Develop Retrieval-Augmented Generation (RAG) pipelines over enterprise vector stores with hybrid retrieval, re-ranking, and grounded evaluation.
• Custom-develop, fine-tune, and deploy large and small language models (LLMs and SLMs) for real-time operational analysis and decision support; build streaming NLP and agentic architectures that integrate with enterprise aviation platforms.
• Develop AI/ML solutions for predictive analytics in aviation safety; probabilistic modeling, time-series and anomaly detection, and causal-factor analysis on ASIAS/FOQA/ASRS and related data.
• Data Engineering & Infrastructure
• Architect, build, and maintain scalable data pipelines for structured, semi-structured, and unstructured data using orchestration tools (e.g., Apache Airflow, Prefect, AWS Glue, or Azure Data Factory).
• Design and implement robust ETL/ELT processes with strong error handling, idempotency, monitoring, and dependency management across cloud and hybrid environments.
• Integrate and manage data across enterprise platforms (e.g., Palantir Foundry, AWS, Azure, GCP); process high-volume data using distributed frameworks (Apache Spark, Flink).
• Thought Leadership & Innovation
• Analyze state-of-the-art technologies, drive cutting-edge AI strategies and architectures, identify emerging trends, gaps, and innovation opportunities.
• Promote thought leadership through publications, conference presentations, and industry collaboration.
• Contribute ideas that support growth and new business opportunities.

• Required Qualifications
• Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related technical field. Recent Ph.D. graduates and postdoctoral researchers with strong applied AI/ML or NLP research backgrounds are strongly encouraged to apply.
• 5+ years of relevant experience in data engineering, applied AI/ML research, or a related technical discipline. Equivalent experience — via a Ph.D./postdoctoral research, federal or military technical service, or startup engineering roles — will be considered in place of traditional industry tenure.
• Proficient in Python with strong software engineering practices: OOP, testing frameworks (pytest), logging, error handling, and version control.
• Expertise in ETL/ELT orchestration (Apache Airflow, Prefect, Luigi, AWS Glue, or Azure Data Factory); deep SQL proficiency including query optimization and index tuning.
• Data modeling expertise: normalization, star/snowflake schemas, slowly changing dimensions (Type 1/2).
• Experience with big data processing frameworks (Apache Spark, Flink) and cloud data ecosystems (AWS, Azure, GCP).
• Hands-on experience custom-developing AI/ML solutions (LLMs/SLMs, real-time voice/speech), and predictive data analytics; pipelines, preprocessing, embedding, grounding, and production deployment.
• Working knowledge of Generative AI and RAG architectures, vector databases, and enterprise data infrastructure integration with model versioning, monitoring, and rollback strategies.
• Fast learner with strong communication skills — able to explain complex technical work clearly to both technical and non-technical audiences, and eager to grow into public speaking and industry engagement.
• Must be located in the U.S. or authorized to work in the U.S.; demonstrable portfolio of production AI/ML or data engineering work required.

• Desired Skills
• FAA domain or Aviation Safety systems exposure a plus but not required (e.g., ASIAS, SWIM, Foundry) — strong candidates from adjacent regulated domains (finance, healthcare, defense) with deep AI/ML skills will also be considered; domain training is provided.
• DevSecOps in regulated or safety-critical environments; experience leading technical architecture discussions
• LLM/SLM fine-tuning using PyTorch, TensorFlow, Hugging Face Transformers (LoRA, QLoRA); MLOps practices: model drift detection, retraining pipelines, deployment monitoring.
• Real-time STT/TTS, NLP, and streaming voice (Whisper, WhisperX, faster-whisper, Wispr Flow, Google Cloud Speech, Azure Speech) with custom models, accent/ATC phraseology adaptation; real-time inference and AI agents/agentic architectures.
• Speech technologies: STT/TTS systems (Whisper, Google Cloud Speech, Azure Speech Services), custom voice models, accent adaptation.
• Probabilistic modeling and Bayesian inference (pgmpy, PyMC, Pyro, Stan); causal inference and graphical models applied to safety precursor analysis.
• Vector databases (Pinecone, Weaviate, ChromaDB, FAISS, pgvector); API integrations (RESTful/GraphQL) and streaming platforms (Kafka, Kinesis, Pulsar).
• Containerization (Docker, Kubernetes), infrastructure-as-code (Terraform, CloudFormation); data visualization
• Computer vision frameworks (PyTorch Vision, OpenCV, Detectron2, Ultralytics YOLO, SAM) and multimodal models (CLIP, LLaVA, GPT-4o Vision) for surveillance, surface, and document imagery.