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About the Role

aion is the Enterprise AI Platform, a full-stack solution for building, fine-tuning, deploying, and forward-deploying AI for enterprises at scale. We are a fast-growing, VC-backed startup led by founders with a track record of successful exits, building the next generation of enterprise AI infrastructure.

Responsibilities

• Work directly at customer sites from factory floors to executive offices conducting discovery workshops and technical assessments to identify high-impact AI opportunities
• Design and architect end-to-end multimodal agent systems (voice + video + text) that leverage aion's distributed GPU infrastructure and managed services
• Build production-grade voice AI systems using STT, TTS APIs, and LLMs deployed on aion's platform
• Develop vision-enabled agents processing real-time video streams using computer vision pipelines on aion's infrastructure
• Implement sophisticated multi-agent orchestration with frameworks like LangChain or LlamaIndex
• Rapidly prototype POCs in 2-4 weeks, coding alongside client teams to validate concepts and iterate based on feedback
• Optimize for sub-500ms latency, natural conversation flow, turn detection, and interruption handling in real-time systems
• Integrate agents directly into customer codebases via REST/GraphQL/WebSocket APIs and custom SDKs (Python, TypeScript)
• Act as trusted technical advisor to customers, shaping AI strategy and guiding roadmap decisions from concept to production
• Design data architectures with efficient processing pipelines and ingestion workflows for training and inference on aion's platform
• Implement RAG systems with vector databases optimizing embedding strategies, chunk sizes, and retrieval methods
• Prepare and validate datasets for fine-tuning, evaluation, and synthetic data generation
• Work with other MLEs, MLOps, SREs to carry out model deployment and productionization
• Implement LLM and agents observability and monitoring tracking token usage, latency, costs, and quality metrics across deployments on aion's infrastructure
• Instrument applications to trace LLM calls, retrieval operations, agent actions, and data flows
• Build evaluation frameworks with offline benchmarks (accuracy, relevance, safety metrics) and online monitoring (user feedback, drift detection)

Requirements

• 3-5+ years of hands-on experience building production AI/ML systems, with 1-2+ years deploying LLM applications to production
• Multimodal AI expertise practical experience building voice agents, vision systems, or conversational AI serving real users
• Strong LLM foundations hands-on with modern foundation models including fine-tuning, prompt engineering, and evaluation methodologies
• Agent framework proficiency production experience with LangChain, LlamaIndex, or similar orchestration frameworks
• Voice AI platform experience built real-time conversational systems with production STT/TTS integration
• Proficiency in Python (production-grade, async programming, type hints) and JavaScript/TypeScript (full-stack development)
• RAG implementation experience built retrieval-augmented generation systems with vector databases
• MLOps & deployment hands-on with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code
• Cloud platforms experience with AWS, Azure, or GCP for ML workloads and infrastructure management
• Exceptional communication ability to explain complex AI concepts clearly to both technical and business stakeholders
• Customer-facing experience in Solutions Architecture, Technical Account Management, or Pre-Sales Engineering is highly desirable
• Computer vision experience working with video processing, object detection, or vision-language models is a plus
• Model fine-tuning practical experience with LoRA/QLoRA, supervised fine-tuning, or RLHF workflows is a plus
• Inference optimization experience with vLLM, TensorRT-LLM, Triton, or model quantization techniques is desirable
• Observability tooling practical experience with LLM monitoring, tracing, and evaluation frameworks is a strong plus
• Familiarity with WebRTC, real-time streaming protocols, and low-latency media processing
• Founder-level ownership and bias for action
• Strong strategic thinking and ability to connect technical decisions to business impact
• Excellent communication and mentoring skills
• Thrives in ambiguity, fast-paced environments, and early-stage startup culture

Skills

• AI Engineering
• Multimodal AI
• LLM Applications
• Production Code
• Technical Solutions Presentation
• AI System Debugging
• Voice Agents
• Video Processing Systems
• Conversational AI
• Business Requirements Translation
• Technical Solutions
• AI Deployment Lifecycle
• Use Case Discovery
• Solution Architecture
• Multimodal Agent Development
• MLOps Pipeline Implementation
• Production Optimization
• Agent Performance
• Observability
• Evaluation
• Voice AI Platforms
• RAG Systems
• LLM Orchestration Frameworks
• Communication Skills
• Customer Empathy
• Enterprise AI
• STT APIs
• TTS APIs
• LLMs
• Computer Vision Pipelines
• Multi-agent Orchestration
• LangChain
• LlamaIndex
• REST APIs
• GraphQL APIs
• WebSocket APIs
• Python SDKs
• TypeScript SDKs
• Data Architecture
• Data Processing Pipelines
• Data Ingestion Workflows
• Vector Databases
• Embedding Strategies
• Chunk Sizes
• Retrieval Methods
• Dataset Preparation
• Dataset Validation
• Fine-tuning
• Synthetic Data Generation
• Model Deployment
• Productionization
• LLM Observability
• Agent Monitoring
• Token Usage Tracking
• Latency Tracking
• Cost Tracking
• Quality Metrics
• Application Instrumentation
• LLM Call Tracing
• Retrieval Operation Tracing
• Agent Action Tracing
• Data Flow Tracing
• Offline Benchmarks
• Online Monitoring
• User Feedback Analysis
• Drift Detection
• Foundation Models
• Prompt Engineering
• Async Programming
• Type Hints
• Full-stack Development
• Docker
• Kubernetes
• CI/CD Pipelines
• Infrastructure-as-Code
• AWS
• Azure
• GCP
• ML Workloads
• Infrastructure Management
• Technical Stakeholder Communication
• Business Stakeholder Communication
• Solutions Architecture
• Technical Account Management
• Pre-Sales Engineering
• Video Processing
• Object Detection
• Vision-Language Models
• LoRA
• QLoRA
• Supervised Fine-tuning
• RLHF Workflows
• vLLM
• TensorRT-LLM
• Triton
• Model Quantization
• LLM Monitoring Tooling
• LLM Tracing Tooling
• LLM Evaluation Frameworks
• WebRTC
• Real-time Streaming Protocols
• Low-latency Media Processing
• Ownership
• Bias for Action
• Strategic Thinking
• Business Impact Analysis
• Mentoring
• Ambiguity Tolerance
• Fast-paced Environment Navigation
• Early-stage Startup Culture Adaptation

Experience Level

• 3-5+ years of experience
• 1-2+ years deploying LLM applications to production

Benefits

• Competitive compensation
• Flexible work options
• Wellness benefits
• Significant ownership and impact with equity reflective of your contributions

About the Company

• aion is the Enterprise AI Platform, a full-stack solution for building, fine-tuning, deploying, and forward-deploying AI for enterprises at scale.
• By abstracting away infrastructure complexity, aion unifies compute orchestration, training workflows, data pipelines, model versioning, and deployment into a streamlined enterprise experience.
• We forward-deploy AI for enterprises, enabling organizations to rapidly implement production-ready AI solutions.
• We're a fast-growing, VC-backed startup led by founders with a track record of successful exits.
• With teams across the US, UK, and India, we're building the next generation of enterprise AI infrastructure and are looking for exceptional people to help us scale.