Global InfoTek, Inc.

Role Description We are seeking a U.S. citizen with demonstrated experience supporting Federal Government contractors in the development of technical proposals and presentations on an as-needed basis. The ideal candidate has the ability to transform complex technical concepts into compelling, innovative, and visually engaging graphics that clearly communicate key messages to government audiences. - Collaborating effectively with proposal teams and technical subject matter experts (SMEs) to map out visual concepts. - Developing high-quality technical diagrams, system architectures, process flows, and infographics. - Designing professional presentation graphics and compliant proposal layouts. - Maintaining extreme responsiveness and delivering high-quality work under aggressive proposal schedules and stringent turnaround requirements. Qualifications - Bachelor's degree in a related field, or an equivalent combination of education and extensive work experience. Requirements - High proficiency in Microsoft PowerPoint and the Adobe Creative Suite (specifically InDesign, Photoshop, and Illustrator). - Demonstrated experience creating technical diagrams, system architectures, process flows, infographics, and proposal layouts for Federal bids. - Proven ability to work effectively under tight, fluctuating deadlines inherent to the GovCon proposal environment. Relevant Certifications - Certifications in Adobe Creative Cloud applications or relevant design credentials are a plus. Company Description Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.

• Proposal planning and timeline management • Compliance management, mapping, and tracking • Content development and rewriting utilizing existing boilerplate materials • Coordination and collaboration with technical contributors and subject matter experts • Comprehensive editing, formatting, and rigorous quality assurance • Final proposal packaging and submission

Role Description The ideal candidate has 10+ years of experience serving as a Senior Proposal Manager for Federal contractors and has successfully managed proposals from kickoff through final submission. Responsibilities include: - Proposal planning and timeline management. - Compliance management, mapping, and tracking. - Content development and rewriting utilizing existing boilerplate materials. - Coordination and collaboration with technical contributors and subject matter experts. - Comprehensive editing, formatting, and rigorous quality assurance. - Final proposal packaging and submission. Qualifications - Bachelor's degree in a related field, or an equivalent combination of education and extensive work experience. - Demonstrated track record of leading winning Federal proposals for DoD and/or civilian agencies. - Excellent writing and editing skills. - Ability to ensure complete compliance with solicitation requirements. Requirements - Minimum 10 years of experience. - US Citizenship required. - Clearance Level: Secret Eligible. - Job Classification: 1099/Consultant. - Location: Remote. Company Description Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.

• Collaborating effectively with proposal teams and technical subject matter experts (SMEs) to map out visual concepts. • Developing high-quality technical diagrams, system architectures, process flows, and infographics. • Designing professional presentation graphics and compliant proposal layouts. • Maintaining extreme responsiveness and delivering high-quality work under aggressive proposal schedules and stringent turnaround requirements.

Role Description GITI is seeking a Principal Software Engineer to support Cyber Operations Research and Development as the technical lead for production software development on a passive RF emitter identification and network analysis from real-time sensor data streams. The candidate will own the architecture, implementation, and delivery of the production pipeline — a stream ingestion, rollup, and post-processing system that operates on NDF (Network Description File) data produced by TDMA network sensors in dense, contested RF environments. This is a hands-on technical leadership role: the Principal Engineer writes code, makes architecture decisions, and is accountable for pipeline performance and reliability in support of real-world cyber operations. Responsibilities - Own the architecture and implementation of the production software pipeline, including stream ingestion, rollup, database write, and batch post-processing components. - Lead a team of Senior Software Engineers in support of real-world cyber operations; assign work, conduct code reviews, enforce quality standards, and provide technical mentorship. - Establish and maintain disciplined software engineering practices: versioning, CI/CD pipelines, unit and integration testing, and documentation standards. - Design and evaluate database and storage architecture for the tactical system and research enclave environments. - Collaborate with the program technical lead to translate research findings and batch optimization algorithms into production pipeline components. - Evaluate and benchmark Python pipeline performance on tactical-box-spec hardware; identify bottlenecks and lead porting of mature components to Rust or C for edge deployment. - Manage and coordinate the tactical system VM environment and stream simulation infrastructure; ensure research VM is not disrupted by development activity. - Define and enforce stream interface contracts between the ingestion layer, database, and downstream consumers. - Evaluate emerging technologies (e.g., DuckDB/Parquet, Polars, message queues) against program requirements and recommend adoption decisions to the technical lead. - Maintain the program's GitLab repository structure, branching strategy, and release management. - Produce clear technical documentation including architecture decision records, interface specifications, and deployment guides. - Support technical reviews and provide written inputs for sponsor deliverables as directed by the program technical lead. Qualifications - Expert-level career professional with broad and deep application of software engineering principles across the full development lifecycle. - Exercises independent judgment in evaluating methods, techniques, and approaches; identifies and resolves complex technical problems with significant program impact. - Provides technical leadership and direction to other engineers. - Bachelor's (or equivalent) with 10+ years of experience. Requirements - Demonstrated experience leading a software engineering team on a production data pipeline or streaming system; ability to set technical direction and mentor junior engineers. - Expert-level Python development, including stream processing, multi-threaded/async architectures, and performance profiling. - Proficiency in one or more compiled or systems languages (Rust, C, C++, or Go) for performance-critical components; experience porting Python to compiled targets. - Hands-on experience designing and implementing relational database schemas and write-intensive data pipelines (MySQL, PostgreSQL, or equivalent). - Experience parsing binary serialization formats such as FlatBuffers or Protocol Buffers in a production context. - Demonstrated ability to benchmark and optimize pipeline throughput on resource-constrained hardware or cloud environment. - Strong proficiency with Linux system administration, remote server management via SSH, and air-gapped development environments. - Experience architecting multi-consumer data systems where a single write path must serve concurrent display, analytics, and batch processing readers. - Proficient in disciplined software engineering practices: GitLab/Git, CI/CD pipeline design, test-driven development, and code review. - Excellent written and oral communication skills; ability to produce architecture decision records and technical documentation for both engineering and leadership audiences. Desired Skills - Experience with TNS (Target Network System) sensor data formats and NDF ICD specifications. - Familiarity with TDMA network protocols, time-division access architectures, and passive RF signal processing concepts. - Experience deploying and operating software on tactical edge hardware co-located with a sensor system. - Experience with lightweight stream or message queue architectures (ZeroMQ, RabbitMQ, or equivalent). - Experience with Polars or DuckDB for high-performance analytical workloads and write-once/read-many storage patterns. - Experience with LLM-assisted software development tools (e.g., Claude Code, GitHub Copilot, JetBrains AI Assistant, or equivalent); demonstrated ability to use AI tools productively for code generation, refactoring, and test case development while maintaining engineering judgment and code quality standards. - Familiarity with AI/ML model inference integration — ability to incorporate batch optimizer outputs into the production pipeline without requiring ML expertise. - Experience with browser-based data visualization or reporting tools (React, D3, or equivalent) as a consumer of pipeline output. - Experience with Jupyter Notebooks and research enclave environments; ability to bridge from research prototype to production code. - Experience with FlatBuffers binary stream replay and simulation infrastructure for pipeline testing. - Familiarity with Rust toolchain and ecosystem for systems-level development on Linux. Relevant Certifications - Certifications in software engineering, computer science, or related fields (e.g., Certified Software Development Professional (CSDP); Certified Secure Software Lifecycle Professional (CSSLP); Red Hat Certified Engineer (RHCE); C++ Certified Professional Programmer (CPP); Professional Software Developer Certification (PSD); etc.).

• 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

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.).

• Support Cyber Operations Research and Development as the technical lead for production software development • Own the architecture, implementation, and delivery of the production pipeline • Lead a team of Senior Software Engineers in support of real world cyber operations • Establish and maintain disciplined software engineering practices • Collaborate with the program technical lead to translate research findings into production pipeline components

Role Description GITI is seeking a Principal Scientist to serve as the senior technical authority on an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Principal Scientist leads independent, hands-on analysis of NDF (Network Description File) sensor datasets, provides technical direction across parallel research threads, and serves as the primary technical advisor to the government sponsor. The role spans the full research lifecycle: - Formulating hypotheses - Writing and executing analytical code in Python and Jupyter notebooks - Interpreting and validating results - Communicating findings to both technical peers and non-specialist stakeholders This is a deeply technical, hands-on position — the Principal Scientist conducts analysis directly and does not delegate technical work as a substitute for personal proficiency. The candidate will work within a small, distributed team operating in air-gapped Linux environments on resource-constrained tactical edge hardware, with no cloud computing. Qualifications - 10+ years of hands-on applied R&D experience in RF systems, signals intelligence, electronic warfare, or related domains. - Proven ability to quickly acquire domain knowledge; specifically in the areas of wireless digital communications and military techniques, tactics, and procedures. - Demonstrated ability to independently develop and execute data analyses in Python or equivalent tools on real sensor datasets; must be capable of writing production-quality analytical code, not merely directing others to do so. - Experience addressing common problems with large quantities of real-world data, such as imputation, noise, bias, and errors. - Track record of working effectively on constrained-hardware edge systems — no cloud, no discrete GPU — with attention to computational efficiency and multi-core, multi-thread performance on x86 platforms. Requirements - Expert-level career professional recognized as a technical authority in RF systems, signals intelligence, or a closely related applied domain. - Exercises broad independent judgment in defining research approach, evaluating methods, and interpreting results. - Operates with minimal supervision; accountable for the scientific integrity and practical relevance of program research outputs. - Advanced degree (MS or PhD) with 10+ years of hands-on applied R&D experience. Desired Skills - Deep familiarity with RF signal characteristics, sensor phenomenology, and the interpretation of passive receiver data — including recognition of processing artifacts, attribution ambiguities, and the limits of sensor-derived measurements. - 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. - Familiarity with TDMA network protocols, emitter identification techniques (CID/PID), and the signal processing challenges of dense, contested electromagnetic environments. - Experience with interferometric direction-finding, TDOA geolocation, or related passive geolocation methods, including practical knowledge of their failure modes and accuracy limitations. - 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 - Professional certifications in data science, signal processing, or related technical fields. - Advanced academic credentials (PhD, MS) in a relevant quantitative discipline are strongly preferred and may substitute for certifications.

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 of their mathematical foundations and common failure modes is more important than operational experience. - 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.). Company Description Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.