AI/ML Engineer - LLM/RAG

THE ROLE :

We are seeking a highly skilled Senior Software Engineer for a contract role to accelerate AI and Machine Learning initiatives within our Test Software and Infrastructure team. You will be brought in to design, build, and deploy custom ML models, autonomous AI agents, and agentic workflows from the ground up. This is a hands-on, execution-focused role where you will have the opportunity to deliver high-impact automated systems that transform our FPGA testing and development cycles.

KEY RESPONSIBILITIES :

- Design, develop, and deploy autonomous AI agents and multi-agent workflows to automate complex infrastructure tasks, test generation, and failure triage.

- Build, train, and implement custom Machine Learning models tailored to our testing environment (e.g., anomaly detection, predictive analytics, and test optimization).

- Architect scalable internal tools that integrate Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) into our existing engineering ecosystem.

- Connect AI agents and ML models to our core data pipelines and databases (Snowflake, MySQL) to ensure they have access to accurate, real-time data.

- Write robust, production-ready code primarily in Python, while interfacing front-end interfaces and AECG software tools.

- Establish MLOps best practices for the project and collaborate closely with the internal engineering team to ensure seamless integration and knowledge transfer.

PREFERRED EXPERIENCE :

- Professional software engineering experience with a strong focus on backend systems, infrastructure, or tooling, with a proven ability to hit the ground running on new projects.

- Hands-on experience building and deploying AI agents and agentic workflows, whether custom-built or using industry frameworks (e.g., LangChain, AutoGen, or similar).

- Proven track record of building, training, and deploying ML models in production environments using PyTorch, TensorFlow, or Scikit-Learn. Deep understanding of LLM APIs (OpenAI, Anthropic, etc.), advanced prompt engineering, and building RAG pipelines.

- Expert-level proficiency in Python and strong knowledge of UNIX/Linux environments.

- Solid understanding of database architecture and data pipelines (SQL, Snowflake, or similar).

- Experience with MLOps practices, including model deployment, monitoring, and lifecycle management.

- Knowledge of compiled programming languages, with C++ experience highly valued.

- Experience building APIs to serve ML models and AI agents (e.g., FastAPI, Flask).

(ref:hirist.tech)