LEO Technologies, LLC

Backend Engineer

• As a Backend Engineer (Application) you will be responsible for building and maintaining our mission-critical backend services that allows our users to gain actionable insight from our unique dataset(s).
• You will be part of the Core Application team, responsible for creating new solutions as our product grows. These solutions could range from non-trivial search/data-mining needs, to, working on solving multi-tenancy challenges when saving and serving customer data.
• You will participate in all stages of our software development and deployment lifecycle. To achieve this will include participating in planning, design, prototyping, execution, shipping and iteration.

Software Engineer – Backend

Full-stack Engineer

• Build and maintain evaluation frameworks for LLMs and generative AI systems tailored to public safety and intelligence use cases.
• Design guardrails and alignment strategies to minimize bias, toxicity, hallucinations, and other ethical risks in production workflows.
• Partner with AI engineers and data scientists to define online and offline evaluation metrics (e.g., model drifts, data drifts, factual accuracy, consistency, safety, interpretability).
• Implement continuous evaluation pipelines for AI models, integrated into CI/CD and production monitoring systems.
• Collaborate with stakeholders to stress test models against edge cases, adversarial prompts, and sensitive data scenarios.
• Research and integrate third-party evaluation frameworks and solutions; adapt them to our regulated, high-stakes environment.
• Work with product and customer-facing teams to ensure explainability, transparency, and auditability of AI outputs.
• Provide technical leadership in responsible AI practices, influencing standards across the organization.
• Contribute to DevOps/MLOps workflows for deployment, monitoring, and scaling of AI evaluation and guardrail systems (experience with Kubernetes is a plus).
• Document best practices and findings, and share knowledge across teams to foster a culture of responsible AI innovation.

Software Engineer, AI/LLM Evaluation and Alignment

• Lead efforts to improve transcription quality by evaluating, testing, and fine-tuning ASR models (both commercial APIs and open-source).
• Build pipelines that handle speaker identification, diarization, multi-language support, and noise-robust transcription in difficult audio environments.
• Develop and maintain services that integrate multiple ASR providers, ensuring resilience and flexibility across transcription workflows.
• Collaborate with platform engineers to ensure seamless ingestion and persistence of transcription outputs in data pipelines.
• Use data wrangling and exploratory analysis to deeply understand transcription accuracy and error patterns. - Explore and apply audio engineering techniques (denoising, voice isolation, codecs, signal processing) to improve speech clarity.
• Deploy and maintain transcription-related services with basic DevOps practices, ensuring scalability and reliability.
• Participate in all stages of the development lifecycle: ideation, design, prototyping, implementation, deployment, and iteration.

Software Engineer, Transcription

NLP Engineer

• Design, build, and optimize AI-powered solutions using LLMs, RAG pipelines, semantic search, GraphRAG, and Agentic AI architectures.;Implement and experiment with the latest advancements in large-scale language modeling, including prompt engineering, model fine-tuning, evaluation, and monitoring.;Collaborate with product, backend, and data engineering teams to define requirements, break down complex problems, and deliver high-impact features aligned with business objectives.;Inform robust data ingestion and retrieval pipelines that power real-time and batch AI applications using open-source and proprietary tools.;Integrate external data sources (e.g., knowledge graphs, internal databases, third-party APIs) to enhance the context-awareness and capabilities of LLM-based workflows.;Evaluate and implement best practices for prompt design, model alignment, safety, and guardrails for responsible AI deployment.;Stay on top of emerging AI research and contribute to internal knowledge-sharing, tech talks, and proof-of-concept projects.;Author clean, well-documented, and testable code; participate in peer code reviews and engineering design discussions.;Proactively identify bottlenecks and propose solutions to improve system scalability, efficiency, and reliability.

Software Engineer – Backend

Software Engineer, AI/LLM Evaluation and Alignment

Software Engineer, Transcription

AI/NLP Engineer