AI Engineer

eClerx•Austin, TX

21d

About The Position

The AI Engineer will drive data-driven decision-making and intelligent automation by designing, optimizing, and deploying Agentic AI solutions and large and small language models (LLMs/SLMs). The role focuses on building robust pipelines, optimizing models for specific hardware constraints, and developing agent orchestration frameworks to address business automation challenges. The position involves deploying machine-learning models and fine-tuned SLMs in production; designing autonomous agent architectures and retrieval-augmented generation systems; building infrastructure for parameter-efficient fine-tuning; and developing automated data pipelines and production monitoring. The AI Engineer collaborates with Data Science, Data Engineering, and DevOps teams, mentors junior engineers, and explains technical trade-offs to clients and project leaders. This is a six-month fixed-term employee position with possible extension and benefits, based in Austin, Texas. The target U.S. base salary is $175,000–$180,000. Compensation depends on relevant experience, knowledge, skills, other job-related qualifications, and geography.

Requirements

At least 5 years of experience in Software Engineering or Machine Learning Engineering.
A proven track record of deploying AI models in production.
Expert proficiency in Python.
Deep experience with PyTorch, TensorFlow, or JAX, and libraries for model adaptation and inference, 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.
Proven ability to optimize models for restricted resource 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 lower-level languages such as C++ or Rust, or with Go, for performance-critical components.
Experience in a cross-functional, client-facing team environment 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, focusing on 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), using techniques such as LoRA, QLoRA, or 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 automated validation layers that support model-output reliability and safety.
Design and implement production monitoring for model drift, inference performance, and resource utilization.
Work with Data Science, Data Engineering, and DevOps teams to transition prototypes into hardened production binaries.
Provide technical guidance and code reviews for junior engineers and promote software engineering and AI deployment best practices.
Translate technical constraints, such as memory limits and inference speed, into clear trade-offs for client stakeholders and project leadership.