Lead AI Engineer

Jobgether

4d • $200,000 - $215,000

About The Position

This is a strategic leadership role at the forefront of applied artificial intelligence, focused on advancing large language model (LLM) systems in real-world, high-scale production environments. You will own the full post-training lifecycle of LLMs, from supervised fine-tuning and preference optimization to deployment, monitoring, and continuous improvement. Working with massive real-time data systems, you will help shape intelligent decision-making engines that operate at extraordinary scale. This role blends deep technical expertise with cross-functional collaboration, enabling you to influence architecture, performance, governance, and safety standards. If you are passionate about improving model behavior through data-driven iteration—not just prompt engineering—this opportunity offers both impact and technical ownership.

Requirements

• Significant hands-on experience leading Supervised Fine-Tuning (SFT) of LLMs in production environments, beyond prompt-only implementations.
• Direct experience with OpenAI APIs and/or AWS Bedrock for post-training, fine-tuning, and deployment workflows.
• Strong expertise in LLM post-training methodologies, including instruction tuning, data preparation, evaluation frameworks, and diagnosing common failure modes.
• Proven experience building and operating agentic LLM systems involving tool use, multi-step reasoning, and workflow orchestration.
• Advanced proficiency in Python and modern machine learning frameworks such as PyTorch.
• Experience deploying and maintaining ML systems in distributed, production-grade infrastructures.
• Strong understanding of trade-offs across model performance, latency, cost efficiency, scalability, and safety.
• Demonstrated ability to lead technically, mentor team members, and drive high-impact AI initiatives from concept through deployment.

Responsibilities

• Lead end-to-end Supervised Fine-Tuning (SFT) initiatives for large language models, shaping reasoning quality, tone, instruction adherence, and domain-specific performance in production systems.
• Extend post-training pipelines through instruction tuning and preference-based optimization techniques (including RLHF-style or direct preference optimization approaches).
• Design, curate, and maintain high-quality datasets for SFT and preference training, leveraging both human-labeled and synthetic data aligned to real-world use cases.
• Own model evaluation and benchmarking frameworks, including offline behavioral evaluations, online A/B testing, regression monitoring, and performance tracking.
• Develop and operate agentic LLM systems supporting multi-step reasoning, tool integration, workflow orchestration, and automated decision execution.
• Optimize prompting strategies, retrieval-augmented generation (RAG), memory systems, and tool-calling mechanisms, understanding when to apply fine-tuning versus prompt-based solutions.
• Collaborate with data engineering, platform, and product teams to integrate fine-tuned models into scalable, high-throughput, low-latency environments.
• Establish best practices for model versioning, deployment, rollback strategies, experimentation, governance, and AI safety standards.
• Provide technical leadership and mentorship to engineers working on applied AI and LLM initiatives.

Benefits

• Competitive salary range of $200,000 – $215,000 USD, based on experience and location
• Employee equity participation
• Unlimited paid time off
• Comprehensive medical, dental, and vision coverage
• Virtual wellness programs and employee discount offerings
• Pet insurance options
• Opportunity to work on cutting-edge AI systems operating at massive scale
• Inclusive, collaborative culture that values innovation and belonging

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed