Senior AI/ML Engineer – LLM Infrastructure, Agentic Workflows, and Synthetic Data

We are looking for an experienced Senior AI/ML Engineer to help design and build a scalable AI platform supporting LLM applications, synthetic data generation, agentic workflows, reinforcement learning environments, and production machine learning operations. This is a hands-on engineering project for someone who is comfortable working across AI/ML systems, distributed infrastructure, cloud services, APIs, and large-scale data pipelines. Project Responsibilities You will help us: Architect scalable systems for synthetic data generation and automated data labeling Build agentic and multi-agent workflows that can reason, take actions, and improve through evaluation Design APIs, backend services, orchestration layers, and compute infrastructure Develop LLM training, evaluation, and inference pipelines Create benchmarks, datasets, simulation environments, and automated evaluation frameworks Provision reinforcement learning environments for AI agents Build reliable data pipelines for collecting, transforming, validating, and processing large datasets Optimize LLM workloads for performance, reliability, scalability, and cost Deploy AI services across AWS, GCP, Azure, or hybrid cloud environments Implement monitoring, telemetry, logging, alerting, and production debugging processes Collaborate with our team on architecture decisions, technical planning, and implementation Required Experience 5+ years of professional software engineering experience Strong Python development experience Experience building production AI/ML platforms or cloud-native software systems Hands-on experience with one or more of the following: LLM applications and infrastructure Agentic or multi-agent workflows Synthetic data generation MLOps and model platforms Training and inference infrastructure Machine learning pipelines Workflow orchestration Large-scale data processing Experience with distributed systems and cloud infrastructure Experience with AWS, GCP, or Azure Strong understanding of APIs, microservices, databases, queues, and asynchronous processing Experience with monitoring, observability, reliability, performance optimization, and production incident debugging Ability to write clean, maintainable, tested, and well-documented code Preferred Technologies Experience with several of the following would be valuable: Python FastAPI, Flask, or Django PyTorch, TensorFlow, Hugging Face, or similar ML frameworks OpenAI, Anthropic, Gemini, or open-source LLM APIs LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar agent frameworks Kubernetes and Docker AWS, GCP, or Azure Apache Spark, Ray, Kafka, or distributed computing frameworks Airflow, Dagster, Prefect, Argo Workflows, or similar orchestration tools PostgreSQL, MongoDB, Redis, or vector databases MLflow, Weights & Biases, or similar experiment-tracking platforms Terraform and CI/CD tools Reinforcement learning and simulation frameworks Expected Deliverables Depending on your experience and the project phase, deliverables may include: AI platform architecture and technical design documentation Production-ready Python services and APIs Synthetic data generation pipelines Agentic workflow and multi-agent orchestration systems LLM evaluation and benchmarking infrastructure Training, inference, and reinforcement learning environments Cloud deployment and infrastructure automation Monitoring, logging, testing, and operational documentation Ideal Freelancer The ideal candidate has previously built or supported production AI systems—not only prototypes or notebooks. You should be able to make practical architecture decisions, communicate technical tradeoffs, work independently, and deliver reliable software for real users. Experience working with high-growth startups, enterprise AI products, or large customer-facing platforms is a strong advantage. Project Details Location requirement: Applicants must currently be located in the United States. Engagement: Full-time, ongoing contract Work arrangement: 100% remote Monthly compensation: $4,000–$6,000, depending on experience and qualifications Experience level: Senior/Expert Availability: Approximately 40 hours per week Communication: Regular progress updates, technical discussions, and team meetings during U.S. business hours How to Apply Please include: A brief description of your experience with production AI/ML systems Examples of LLM, agentic AI, synthetic data, MLOps, or distributed systems projects you have completed Your specific role and contributions to those projects The cloud platforms and AI frameworks you have used Your GitHub, portfolio, or relevant project links Your hourly rate and weekly availability Please begin your proposal with “AI Platform” so we know you have reviewed the full project description.