Overview

We are seeking a Senior AI Engineer to help shape and scale advanced AI capabilities powering intelligent content analysis for a global audience of gaming creators. This role covers the full AI development lifecycle, including research, prototyping, model development, deployment, optimization, and monitoring in production environments.

You will work extensively with multimodal AI technologies that combine video, audio, text, and gameplay signals, while leveraging modern language models, vision-language models, agent-based architectures, and scalable machine learning infrastructure. The role requires close collaboration with leadership, product teams, and software engineers to transform business challenges into impactful AI-driven solutions. We also encourage the effective use of AI-assisted tools to improve productivity and accelerate execution.

Key Responsibilities

• Develop and maintain multimodal AI systems that analyze and correlate video, audio, transcripts, gameplay data, and textual information to generate actionable insights.

• Architect and implement AI agent workflows using frameworks such as LangGraph, CrewAI, AutoGen, or internally developed orchestration solutions, including adoption of the Model Context Protocol (MCP) for tool integration and external connectivity.

• Deploy AI services into production environments through microservice architectures, API-based integrations, event-driven pipelines, and model serving platforms such as vLLM, SGLang, TensorRT-LLM, TGI, and vector databases.

• Improve inference efficiency through approaches such as model quantization (GPTQ, AWQ, GGUF), knowledge distillation, speculative decoding, and other optimization techniques.

• Build evaluation systems, monitoring solutions, and AI safety mechanisms covering prompt security, output validation, privacy protection, and content moderation.

• Take ownership of the end-to-end highlight detection ecosystem, combining video, audio, transcript, and gameplay signals while determining optimal routing strategies and compute utilization.

• Lead the lifecycle of vision-language model deployment, including prompt design, performance assessment, failure analysis, production readiness evaluation, and model fine-tuning on proprietary datasets.

• Manage dataset preparation, supervised fine-tuning (SFT), reinforcement learning workflows, experiment tracking, and model iteration processes.

• Create game-specific testing frameworks with detailed error categorization, benchmark maintenance, and release gatekeeping before production deployment.

• Design resilient AI services with observability, safety controls, graceful degradation strategies, and scalable integration patterns.

• Establish continuous feedback loops by collecting production corrections, expanding labeled datasets, and incorporating new signals into future training and evaluation cycles.

• Work extensively with media processing technologies including shot segmentation, frame extraction, codec-aware processing, quality analysis, and optimization of visual quality versus performance trade-offs.

• Provide technical mentorship to junior team members and clearly communicate AI performance, infrastructure costs, and engineering trade-offs to both technical and non-technical stakeholders.

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline, or equivalent practical experience.

• At least 5 years of experience delivering AI solutions in production environments, including a minimum of 2 years working directly with large language models, vision-language models, or generative AI technologies.

• Demonstrated experience building and operating production-grade AI systems.

• Strong hands-on expertise in fine-tuning, deploying, and managing modern LLMs and VLMs, including dataset preparation, SFT/RL workflows, and experiment management.

• Deep understanding of agentic AI concepts including tool invocation, memory management, workflow orchestration, state handling, and multi-agent systems.

• Advanced prompt engineering capabilities, including structured outputs, few-shot techniques, and constrained generation strategies.

• Experience with multimodal AI applications involving video understanding, audio processing, cross-modal reasoning, and large-scale media pipelines.

• Familiarity with frame extraction workflows, video processing systems, and scalable media infrastructure.

• Ability to design custom evaluation methodologies beyond standard benchmarks, including domain-specific testing, failure analysis, regression monitoring, and deployment gating.

• Proven experience optimizing model serving costs through quantization, batching, adaptive compute allocation, spot infrastructure, or similar approaches.

• Strong communication and documentation skills with the ability to explain technical concepts, model behavior, cost implications, and performance trade-offs to business stakeholders.

• Excellent analytical and problem-solving abilities, including making informed decisions in situations with incomplete information.

Preferred Qualifications

• Experience working with advanced reasoning models such as o3/o4-class models, DeepSeek-R1, QwQ, or similar systems, as well as test-time compute optimization techniques.

• Familiarity with gaming, livestreaming, esports, or creator-focused platforms.

• Experience developing real-time video processing solutions using technologies such as FFmpeg or GStreamer.

• Knowledge of edge or on-device AI deployment using frameworks such as ONNX, CoreML, or mobile inference solutions.