Lead Applied AI Engineer
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Job Description
Lead AI Engineer managing the design and delivery of production-grade AI systems for Gramian Consultancy. Focusing on engineering leadership and applied machine learning in a remote environment.
Responsibilities:
- Lead and scale engineering teams delivering production AI systems and platforms
- Drive architecture and implementation of LLM-powered applications, including RAG, embeddings, fine-tuning, and evaluation pipelines
- Establish best practices for LLM testing, benchmarking, grounding, hallucination detection, and performance validation
- Partner with product and business leaders to translate AI opportunities into scalable technical solutions
- Oversee deployment and lifecycle management of AI models in cloud production environments
- Guide development of robust data pipelines, integration patterns, and MLOps workflows
- Mentor engineering leaders and senior engineers, fostering strong technical ownership and execution culture
- Ensure systems meet enterprise standards for reliability, security, privacy, and scalability
Requirements:
- 8+ years of software engineering experience including leadership of engineering teams
- Strong recent experience applying AI/LLM systems to real production products and workflows
- Proven success building and scaling engineering teams in production AI environments
- Deep experience with modern LLM ecosystem including:
- embeddings
- RAG architectures
- RLHF
- fine-tuning approaches
- Experience designing or implementing LLM evaluation frameworks, including prompt testing, grounding strategies, hallucination mitigation, and benchmarking
- Strong programming experience in Python and/or Typescript/Javascript
- Excellent communication skills with experience leading cross-functional initiatives
- **Preferred Qualifications**
- Experience working with large-scale datasets in AdTech or advertising platforms
- Experience with Snowflake, Databricks, or similar large-scale data platforms
- Hands-on experience with AI frameworks such as PyTorch, TensorFlow, or Hugging Face
- Experience deploying models into production cloud environments
- Familiarity with data pipelines, integration patterns, and MLOps practices
- Direct experience implementing RAG systems at scale and/or fine-tuning large language models
- Exposure to diffusion, image, or video generation models
- Experience with creative pipelines or AI-driven content workflows
- Background in statistical modeling or mathematics
- Awareness of AI privacy, copyright, or ethical considerations
- Experience in media, entertainment, or content-tech industries
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