Forward Deployed ML Engineer, Agents

About this role

About aion Aion is the enterprise AI platform, a full-stack solution for building, fine-tuning, and deploying AI at scale. Whether an organization is modernizing internal operations, launching AI-powered products, or transforming customer experiences, Aion takes them from concept to production on a single, unified platform. We work differently than most AI companies: our teams deploy alongside our customers, turning production-ready AI into real business outcomes in weeks, not quarters. 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 and we’re looking for exceptional people to help us scale.

Who You Are You're a hands-on AI engineer with 3-5+ years of experience building production-grade multimodal AI systems and LLM applications. Your responsibilities mirror those of a hands-on AI startup CTO you work in small teams to own delivery of high-stakes customer projects, embedding directly at client sites to architect, build, and deploy intelligent agent solutions. You're equally comfortable writing production code, presenting technical solutions to C-level executives, and debugging complex AI systems on factory floors or in customer data centers. You've shipped voice agents, video processing systems, or conversational AI to production. You thrive translating ambiguous business requirements into concrete technical solutions that create measurable impact. You're comfortable working across the full AI deployment lifecycle from use case discovery and solution architecture to multimodal agent development, MLOps pipeline implementation, and production optimization. You understand what makes agents perform well in production and how to systematically improve quality through observability and evaluation. Experience with voice AI platforms, RAG systems, and LLM orchestration frameworks is highly desirable. You bring exceptional communication skills, customer empathy, and the drive to build AI solutions that transform enterprise operations globally.

What You'll Do Customer Engagement & Multimodal Agent Development 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(or similar) frameworks like LangChain or LlamaIndex—enabling tool use, memory management, and autonomous task completion 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 Data Strategy & MLOps Infrastructure 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 Observability, Evaluation & Production Operations 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 Technical Skills & Experience 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,…