AI Engineer
About The Position
The AI Engineer will drive data-driven decision-making and intelligent automation through the architectural design, optimization, and deployment of Agentic AI solutions and Large Language Models (LLMs/SLMs). The role focuses on building robust pipelines, optimizing model performance for specific hardware constraints, and developing agent orchestration frameworks to solve business automation challenges. The position includes integrating and optimizing machine learning models and fine-tuned SLMs for production, building autonomous agent workflows, implementing parameter-efficient fine-tuning, and developing retrieval-augmented generation systems. It also involves data pipeline development, model quality assurance and monitoring, cross-functional integration, mentoring junior engineers, and communicating technical trade-offs to client stakeholders and project leadership. This is a six-month fixed-term employee position with possible extension and benefits, located in Austin, Texas.
Requirements
- At least 5 years of experience in Software Engineering or Machine Learning Engineering, with a proven record of deploying AI models in production.
- Expert proficiency in Python.
- Deep experience with PyTorch, TensorFlow, or JAX, and model adaptation and inference libraries such as the Hugging Face ecosystem.
- Hands-on experience with SQL/NoSQL databases, vector databases, and cloud-native AI services such as AWS, GCP, or Azure.
- Demonstrated mastery of Git, CI/CD pipelines, Docker/Kubernetes, and REST/gRPC API design.
- Ability to optimize models for resource-constrained environments, including memory and CPU/GPU limits, without compromising core performance.
- Deep experience with PEFT libraries, such as Hugging Face PEFT, and fine-tuning frameworks.
Nice To Haves
- Familiarity with C++, Rust, or Go for performance-critical components.
- Experience in cross-functional, client-facing teams within an outsourcing or consulting framework.
- Familiarity with workflow automation and enterprise analytics tools, such as Alteryx and advanced BI pipelines.
Responsibilities
- Lead end-to-end integration of machine learning models and fine-tuned SLMs into production, including model compression, latency reduction, and hardware-specific optimization.
- Design and implement autonomous agent architectures, including multi-step reasoning engines, tool-use integration, and structured decision-making frameworks.
- Develop and maintain infrastructure for Parameter-Efficient Fine-Tuning (PEFT), including techniques such as LoRA, QLoRA, and adapter tuning.
- Build and maintain high-performance vector databases and semantic search indices for context-aware AI responses and sub-second data retrieval.
- Develop scalable, automated pipelines for cleaning, normalizing, and feature engineering high-velocity raw data streams.
- Collaborate with Data Scientists to establish ground-truth datasets and implement automated validation layers for model output reliability and safety.
- Design and implement monitoring solutions for model drift, inference performance, and production resource utilization.
- Collaborate with Data Science, Data Engineering, and DevOps teams to move models from prototype to hardened production deployment.
- Provide technical guidance and code reviews for junior engineers, promoting software engineering and AI deployment best practices.
- Explain technical constraints and trade-offs to client stakeholders and project leadership.
Benefits
- Benefits are included with the fixed-term employee position; specific benefits are not listed.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
