
1 - 10 employees
đź’Ľ Consulting
📣 Marketing
📦 Logistics
Consulting • Marketing • Logistics
Rockstar is a full-service recruitment company that leverages a blend of human expertise and artificial intelligence to help businesses hire better and faster at a lower cost. They offer a comprehensive recruitment service for less than $1,500 per role by providing access to a large talent database, fast and efficient candidate reviews, and human screening. Their proprietary AI enables the review of thousands of applications to quickly match candidates to job descriptions, while custom screening calls are conducted by recruiters in the US, UK, or Australia. Rockstar supports hiring across a wide range of professional roles, including sales, marketing, strategy, product, business operations, analytics, data science, software development, infrastructure, finance, and accounting. This flexible approach allows companies to meet strong candidates without long-term commitments, making it a cost-effective solution for teams of all sizes.
đź•’ May 20
Improve your chances of getting an interview by checking your resume score before you apply.

1 - 10 employees
đź’Ľ Consulting
📣 Marketing
📦 Logistics
Consulting • Marketing • Logistics
Rockstar is a full-service recruitment company that leverages a blend of human expertise and artificial intelligence to help businesses hire better and faster at a lower cost. They offer a comprehensive recruitment service for less than $1,500 per role by providing access to a large talent database, fast and efficient candidate reviews, and human screening. Their proprietary AI enables the review of thousands of applications to quickly match candidates to job descriptions, while custom screening calls are conducted by recruiters in the US, UK, or Australia. Rockstar supports hiring across a wide range of professional roles, including sales, marketing, strategy, product, business operations, analytics, data science, software development, infrastructure, finance, and accounting. This flexible approach allows companies to meet strong candidates without long-term commitments, making it a cost-effective solution for teams of all sizes.
• 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.
• 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.
Apply Nowđź•’ May 20
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