• Design and ship AI agent systems end to end, from early experiments through to production, owning the data access layer, retrieval strategy, and integration with Kota's existing infrastructure including Metabase and internal APIs • Build and own the evaluation framework from scratch, including golden datasets, regression harnesses, and the ability to distinguish retrieval from generation failures; think adversarially about prompt injection, data boundary violations, and silent failure modes in a regulated context • Set the technical direction for AI at Kota, with a seat at the table on agent architecture, and where AI creates the most leverage across the product • Work across the pod and Platform team to surface new AI opportunities, collaborate with the AI Product Lead, and raise the bar on engineering practice through peer review and documentation • Pick up slack and expand the role, this is an early pod with room to shape it; the remit will grow with you

• Define the AI engineering roadmap and architecture standards across the organization • Lead build vs. buy vs. integrate decision-making for AI systems and platforms – and be accountable for articulating how you arrived at those decisions • Evaluate emerging tools, frameworks, and models; provide clear, defensible recommendations to leadership • Serve as the ultimate technical escalation point for complex AI/ML system design challenges • Architect AI solutions for our enterprise platform • Design systems for scale, reliability, and cost-efficiency in production environments • Establish LLMOps practices • Ensure AI systems integrate cleanly with existing product and engineering infrastructure • Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes • Receive operational support to help initiate and coordinate cross-functional engagement • Provide clear guidance on resource allocation • Translate complex technical concepts for non-technical audiences • Champion and introduce AI-assisted coding practices and developer tooling across the engineering organization • Define standards for responsible AI development including guardrails, evaluation frameworks, and security considerations • Drive continuous improvement in how the organization builds and ships AI-powered features

• Define the AI engineering roadmap and architecture standards across the organization • Lead build vs. buy vs. integrate decision-making for AI systems and platforms • Architect AI solutions for the enterprise platform • Design systems for scale, reliability, and cost-efficiency in production environments • Establish LLMOps practices • Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes • Champion and introduce AI-assisted coding practices and developer tooling across the engineering organization

Role Description GITI is seeking a Senior AI/ML Engineer to support an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Senior AI/ML Engineer designs, builds, and validates machine learning models for RF emitter identification, conducts hands-on exploratory data analysis on NDF (Network Description File) sensor datasets, and implements ML data pipelines that operate on constrained tactical edge hardware. Working under the direction of the Principal AI/ML Engineer and program technical lead, the candidate collaborates closely with research scientists and software engineers to translate analytical findings into reproducible, well-documented ML experiments and pipeline components. The role requires strong Python and deep learning skills, comfort with real-world noisy sensor data, and the ability to work in air-gapped Linux environments without cloud infrastructure or GPU acceleration. Responsibilities - Design, build, and validate machine learning models for RF emitter identification, including feature engineering from sensor data, training pipeline development, model evaluation, and iterative refinement based on results. - Conduct hands-on exploratory data analysis on RF sensor datasets using Python and Jupyter notebooks, writing and running analytical code, characterizing feature distributions, identifying data quality issues, and producing documented findings. - Implement and maintain ML data pipelines, ingesting NDF sensor streams, applying rollup and preprocessing logic, constructing training datasets, and ensuring pipeline correctness on constrained edge hardware with no cloud dependency. - Collaborate with the technical lead and Principal AI/ML Engineer to investigate RF sensor data quality, attribution reliability, and feature behavior under contention, writing code to characterize error sources, validate assumptions, and reproduce findings. - Produce clear technical documentation of experiments, model configurations, and results, maintaining reproducibility through disciplined versioning, and contributing to monthly status reports and team knowledge sharing. Qualifications - Bachelor’s or Master’s (or equivalent) with 5–7 years of hands-on applied experience. Requirements - 5+ years of hands-on applied experience in machine learning, data science, or RF signal processing. - Demonstrated proficiency in Python for ML and data science work — PyTorch or TensorFlow for model development, Pandas/NumPy for data manipulation, and scikit-learn or similar for evaluation and baseline modeling. - Hands-on experience designing, training, and evaluating deep learning models, particularly metric learning, Siamese networks, or other similarity-learning architectures on real-world, noisy, imbalanced datasets. - Practical experience handling real-world data quality problems — missing values, label noise, class imbalance, systematic bias, and sensor artifacts — and the ability to diagnose and address them without discarding valid data. - Ability to develop and run ML pipelines on Linux-based systems without cloud infrastructure or GPU acceleration, optimizing for CPU-only inference and multi-threaded data processing on resource-constrained x86 hardware. Desired Skills - Familiarity with RF signal characteristics, passive receiver phenomenology, and sensor data interpretation, including awareness of processing artifacts, attribution ambiguities, and measurement limits common in signals intelligence datasets. - Hands-on experience applying machine learning, particularly metric learning, deep learning networks, or similarity-learning architectures to RF or time-series signal data, including feature engineering, training pipeline development, and model validation. - Exposure to TDMA network protocols or military datalink systems, and interest in learning the signal processing challenges of dense, contested electromagnetic environments. - Familiarity with direction-finding, time-difference-of-arrival (TDOA), or related passive geolocation concepts, understanding their mathematical foundations and common failure modes. - Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware. - Background in statistical signal processing — error ellipses, bearing estimation uncertainty, feature reliability under noise — with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization. Relevant Certifications - Certifications in machine learning, data science, or related technical fields (e.g., TensorFlow Developer Certificate; PyTorch Certified Associate; AWS Certified Machine Learning — Specialty; Microsoft Certified: Azure AI Engineer Associate; Certified Analytics Professional (CAP); etc.).