Overview

Job Title: AI Systems Engineer

Responsibilities

• We’re seeking an AI Systems Engineer who thrives at the intersection of AI model optimization, infrastructure engineering, and applied research. You will evaluate, host, and optimize a wide range of AI models—spanning ASR, LLMs, and multimodal systems—and build the orchestration layer that powers scalable, low-latency deployments.
• This is a role for someone who’s comfortable navigating ambiguity, researching emerging AI methods, and translating client requirements into robust, production-ready solutions.
• You’ll work across the full stack—from GPU inference tuning to React-based control dashboards—building a resilient and scalable AI delivery platform.
• AI Model Evaluation & Optimization
• Evaluate, benchmark, and optimize AI models (speech, text, vision, multimodal) for latency, throughput, and accuracy.
• Implement advanced inference optimizations using ONNX Runtime, TensorRT, quantization, and GPU batching.
• Continuously research and experiment with the latest AI runtimes, serving frameworks, and model architectures.
• Develop efficient caching and model loading strategies for multi-tenant serving.
• AI Infrastructure & Orchestration
• Design and develop a central orchestration layer to manage multi-model inference, load balancing, and intelligent routing.
• Build scalable, fault-tolerant deployments using AWS ECS/EKS, Lambda, and Terraform.
• Use Kubernetes autoscaling and GPU node optimization to minimize latency under dynamic load.
• Implement observability and monitoring (Prometheus, Grafana, CloudWatch) across the model-serving ecosystem.
• DevOps, CI/CD & Automation
• Build and maintain CI/CD pipelines for model integration, updates, and deployment (GitHub Actions, CodePipeline, etc.).
• Manage Dockerized environments, version control, and GPU-enabled build pipelines.
• Ensure reproducibility and resilience through infrastructure-as-code and automated testing.
• Frontend & Developer Tools
• Create React/Next.js-based dashboards for performance visualization, latency tracking, and configuration control.
• Build intuitive internal tools for model comparison, experiment management, and deployment control.
• Utilize Cursor, VS Code, and other AI-powered development tools to accelerate iteration.
• Client Interaction & Solutioning
• Work closely with clients and internal stakeholders to gather functional and performance requirements.
• Translate abstract business needs into deployable AI systems with measurable KPIs.
• Prototype quickly, iterate with feedback, and deliver robust production systems.
• Research & Continuous Innovation
• Stay on top of the latest AI research and model releases (OpenAI, Anthropic, Hugging Face, Meta, etc.).
• Evaluate emerging frameworks for model serving, fine-tuning, and retrieval (LangChain, LlamaIndex, GraphRAG, etc.).
• Proactively identify and implement performance or cost improvements in the model serving stack.
• Share learnings and contribute to the internal AI knowledge base.

Qualifications

• Strong proficiency in Python, TypeScript/JavaScript, Bash, and modern software development practices.
• Deep understanding of Docker, Kubernetes, Terraform, and AWS (ECS, Lambda, S3, CloudFront).
• Experience with inference optimization (ONNX, TensorRT, quantization, batching).
• Proven ability to design and scale real-time inference pipelines.
• Experience building and maintaining CI/CD pipelines and monitoring systems.
• Hands-on experience with React/Next.js or similar frameworks for dashboard/UI development.
• Strong grasp of API design, load balancing, and GPU resource management.

Nice to have

• Experience with LangChain, LlamaIndex, GraphRAG, or vector databases (FAISS, Neo4j).
• Familiarity with speech processing models (Whisper, Silero, NeMo, etc.).
• Prior work with serverless inference or edge AI architectures.
• Knowledge of data pipelines, model versioning, and MLOps best practices.
• Excellent problem-solving in ambiguous, evolving environments.
• Strong ability to research, self-learn, and prototype emerging AI technologies.
• Confident communicator who can translate technical findings to business impact.
• Ownership mindset with a collaborative, solution-oriented approach.