Lead AI Engineer
New
Based in United StatesFull-TimeLead
Salary198,000 - 261,000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonCloud ComputingMachine LearningCI/CDGenerative AIDistributed Systems
Requirements
- 8+ years of experience in software engineering, AI engineering, or machine learning roles, including leadership responsibilities in technical teams.
- Strong expertise in Python and modern software engineering practices, including CI/CD, testing frameworks, version control, and distributed system design.
- Proven experience designing and deploying end-to-end AI systems in production, ensuring scalability, reliability, and maintainability.
- Deep knowledge of generative AI, LLMs, and agentic frameworks, with hands-on experience building RAG pipelines and integrating vector databases.
- Experience deploying AI/ML solutions on cloud platforms using containers, orchestration tools, and automated CI/CD pipelines.
- Strong understanding of LLMOps, observability, monitoring, and production AI system lifecycle management.
- Experience with model fine-tuning, adaptation, and ML/NLP frameworks for production-grade applications.
- Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders and influence senior decision-makers.
- Demonstrated leadership ability to mentor engineers, foster collaboration, and guide technical direction in fast-paced environments.
- Strong problem-solving skills with the ability to navigate ambiguity, align stakeholders, and drive consensus on technical decisions.
Responsibilities
- Lead and mentor a cross-functional team of software engineers, data scientists, and AI specialists delivering GenAI-powered solutions in production environments.
- Define technical architecture and engineering direction for scalable, reliable, and maintainable AI systems across multiple initiatives.
- Guide the design and optimization of end-to-end AI solutions, ensuring strong performance, cost efficiency, and production readiness.
- Collaborate with product managers, designers, and business stakeholders to translate business needs into robust AI-driven technical solutions.
- Establish and promote best practices in AI engineering, including testing, monitoring, responsible AI, guardrails, documentation, and CI/CD integration.
- Provide architectural oversight across AI systems, balancing rapid experimentation with disciplined delivery in iterative, safe release cycles.
- Review designs, code, and system implementations to ensure technical quality, consistency, and adherence to engineering standards.
- Drive optimization strategies for GenAI applications, including model tuning, RAG pipelines, and LLM performance improvements.
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