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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** TWENTY TECHNOLOGIES INC. - **Location:** United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Cloud Computing, Database Theory, Software Debugging, Graph Database, Python (Programming Language), Language Modeling, Tensorflow, Software Deployment, Software Engineering, Systems Integration, Management of Software Versions, Google Cloud, Pytorch, Large Language Models, Prompt Engineering, Deep Learning, Kubernetes, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Docker - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/applied-ai-engineer-twenty-8101887 ## About the Role * You're motivated by real-world outcomes and want your work to directly impact national security missions. * You care about rigor: clean data, measurable evaluation, and repeatable experiments beat demos. * You balance research curiosity with product instincts-you ship, observe, iterate, and harden. * You're comfortable working across cloud and on-premises constraints and adapting to the environment. * You communicate clearly with engineers and non-ML partners, and you write documentation people use. * You think in systems: models, retrieval, infrastructure, and feedback loops all have to work together. * You thrive in fast-moving teams with high standards, direct feedback, and high ownership., * You have 4+ years of professional software development experience building and supporting ML/AI-enabled applications. * You have strong Python skills and deep learning experience with PyTorch, TensorFlow, or JAX. * You have hands-on experience with LLM post-training methods (e.g., continued pre-training, SFT, RLHF, DPO, PPO, GRPO). * You have experience curating, cleaning, and preprocessing datasets for training and evaluation. * You have working knowledge of relational, graph, and vector database concepts. * You have experience designing or using evaluation metrics and testing procedures for LLMs and agents. * You have experience integrating LLM/agent systems using frameworks like Pydantic-AI, LangChain/LangGraph, or CrewAI. * You have a Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent practical experience). Nice To Have * You have deployed models to production and supported them through real-world usage and incidents. * You have experience with distributed training systems and performance debugging at scale. * You have implemented quantization or other optimization techniques to improve inference efficiency. * You have strong prompt engineering and model alignment instincts for reliability and control. * You have experience building MLOps/LLMOps/AgentOps practices (versioning, rollout, monitoring). Tech Environment (You Might Work With) * Deep learning stacks: PyTorch, TensorFlow, JAX * LLMOps and serving: vLLM, TensorRT, ONNX * Retrieval and storage: pgvector, ChromaDB, Pinecone, Milvus, Weaviate; relational/graph databases * Orchestration: Pydantic-AI, LangChain/LangGraph, CrewAI * Infra: Docker, Kubernetes; cloud platforms (AWS, GCP, Azure) * Experiment and artifact tracking: dataset/prompt/model versioning Security / Work Environment Must be eligible to obtain and maintain a U.S. Government security clearance. ## Description You'll build and ship language-model-powered systems that strengthen Twenty's mission-critical cyber capabilities for U.S. national security. You'll own the end-to-end workflow-from curating specialized datasets and post-training models to deploying reliable inference and retrieval systems in production. You'll partner closely with product and engineering to translate real operational needs into high-performing AI features, operating across cloud and on-premises environments where speed, correctness, and security matter., * Create, clean, and maintain high-quality training and evaluation datasets for specialized AI use cases. * Fine-tune language models (small specialized through medium foundation models) for mission needs. * Implement post-training and alignment approaches to improve task performance and reliability. * Build retrieval-augmented generation (RAG) systems that ground model outputs in external knowledge. * Develop and optimize model serving infrastructure for production deployments. * Design evaluation frameworks and test harnesses to measure quality, latency, and regressions. * Integrate AI capabilities into applications and workflows using modern orchestration frameworks. * Collaborate with cross-functional partners to identify high-leverage use cases and deliver solutions. * Produce clear technical documentation for models, datasets, and operational processes. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)