Senior AI Engineer
At a glance
Senior AI Engineering role at Rockstar. Remote, United States · full-time · no salary stated · we estimate $161,745–$225,300 across the United States.
Rockstar has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run production LLM apps, agent workflows, RAG pipelines and AI search features
- Improve retrieval with embeddings, vector stores, semantic search and reranking
- Adapt and assess language models for specific needs, using fine-tuning or prompt methods
- Create automated checks for model quality, retrieval accuracy, reliability, latency and cost
- Guide technical design, mentor engineers and move AI prototypes into dependable product features
What you bring
- At least five years in machine learning engineering, AI engineering or a related technical role
- At least two years building or shipping production GenAI, LLM or AI-powered systems
- Advanced Python skills and experience delivering maintainable software for production
- Hands-on use of PyTorch, TensorFlow, Hugging Face Transformers or another similar ML framework
- Experience deploying AI services with Docker, Kubernetes, CI/CD, APIs and cloud infrastructure
Who this fits
This senior role suits an engineer who can take complex AI work from initial design through production and guide other engineers. It is a full-time remote position for someone based in the United States.
From the employer
Rockstar is recruiting for a data intelligence platform that designs, builds, and deploys production-grade AI systems. They are a team of dynamic and savvy professionals who know how to create killer AI applications. Our lean structure and remote team mean we can move fast while still delivering top-notch technology and design.
Position Summary
The client is seeking a Sr. AI Engineer/Sr. Machine Learning Engineer to design, build, deploy, and maintain production-grade AI systems across their data intelligence platform. This role will lead the development of LLM-powered applications, agentic workflows, retrieval-augmented generation systems, model evaluation pipelines, and scalable AI services.
The ideal candidate combines strong machine learning expertise with practical production engineering experience. This person will own complex technical work from concept through deployment, mentor other engineers, and help define best practices for building reliable, observable, and secure AI systems.
Essential Responsibilities
- Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities.
- Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines.
- Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources.
- Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods.
- Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost.
- Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review.
- Translate prototypes and research concepts into reliable product features that can scale in production.
- Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs.
- Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices.
- Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation.
Required Qualifications
- 5+ years of experience in machine learning engineering, AI engineering, data science engineering, or a related technical role.
- 2+ years of experience building or shipping production GenAI, LLM, or AI-powered systems.
- Advanced Python programming skills and experience building maintainable production software.
- Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, or similar ML frameworks.
- Experience with LLM applications, RAG systems, embeddings, vector databases, prompt engineering, and model evaluation.
- Experience deploying AI / ML services using Docker, Kubernetes, CI/CD workflows, APIs, and cloud-native infrastructure.
- Strong understanding of classical machine learning, deep learning, NLP, information retrieval, and model validation.
- Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders.
- Experience mentoring engineers, reviewing technical designs, or leading complex AI engineering initiatives.
Preferred Qualifications
- Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field.
- Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools.
- Experience with model-serving platforms such as vLLM, BentoML, Triton, Ray Serve, or similar systems.
- Familiarity with ML observability, experiment tracking, model monitoring, and prompt/version management tools.
- Experience with graph-based retrieval, knowledge graphs, multimodal models, large-scale data processing, or security-focused data products.
- Experience with infrastructure-as-code, workflow orchestration, model routing, caching, batching, or quantization.
Special Skills or Experience Required
- Proven experience building and deploying production GenAI systems, including LLM applications, agentic workflows, and RAG pipelines.
- Advanced Python and ML framework experience, including PyTorch, TensorFlow, Hugging Face Transformers, or similar tools.
- Experience with LLM fine-tuning, prompt engineering, embeddings, vector databases, semantic search, and model evaluation.
- Strong production engineering skills, including Docker, Kubernetes, CI/CD, model serving, observability, latency optimization, and technical leadership.
Success Measures
Success in this role will be measured by the delivery of reliable AI capabilities, improved model quality, reduced latency and cost, stronger evaluation coverage, improved observability, and the successful mentorship of other engineers. The role should help increase the speed and confidence with which the company can move AI features from prototype to production.