AI infrastructure · agents · research systems
Vladimir Miasoedov
AI Systems Architect
Helping teams build production-ready AI systems that scale beyond prototypes.
I help teams design and launch AI systems that are viable beyond the prototype stage.
The focus is on getting from idea to a working, maintainable system faster — without sacrificing correctness.
Reduce the time from concept to a working AI system.
Who I am
I’m an AI entrepreneur and systems architect working at the intersection of LLM agents, R&D automation, scientific tooling, and complex systems.
I build AI systems at the intersection of research and production.
My focus is on architectures that remain understandable, extensible, and reliable as they grow.
- Multi-agent orchestration · planning · execution
- RAG + graphs + memory systems for research
- AI infrastructure for GPU workloads & pipelines
- Deep-tech MVPs → scalable products
What I build
Systems where research depth meets engineering discipline: agents, tooling, and infrastructure designed to hold up under real complexity.
AI Agents & Multi-Agent Systems
- Autonomous LLM agents
- Research, planning, execution, reasoning
- LangGraph / CrewAI / custom architectures
Scientific & R&D Platforms
- AI for science
- Knowledge graphs
- Hypothesis generation & research copilots
AI SaaS & Products
- MVP → scalable SaaS
- APIs, platforms, internal tools
- B2B / B2B2C
Automation & AI Infrastructure
- Kubernetes
- GPU workloads
- AI pipelines · CI/CD for AI systems
AI-Powered Content & Media
- Generative video
- AI pipelines for content
- Creative automation
Complex Systems Engineering
- Systems thinking
- Observability & reliability
- Long-term maintainability
Core skills
Draft overview — refined per project, constraints, and stack.
LLM & Agent Engineering
- Tool use, function calling, structured outputs
- Planning/execution loops, guardrails, state machines
- Long-context workflows, caching, cost/latency control
- Prompting systems & prompt versioning
RAG, Memory, Knowledge
- Embeddings, retrieval, reranking, hybrid search
- Vector DBs + document pipelines (chunking, ETL)
- Graph reasoning (knowledge graphs), memory layers
- Evaluation: relevance, faithfulness, citation discipline
ML Systems & Evaluation
- Experiment design, ablation, error analysis
- Offline/online evaluation, golden sets, benchmarks
- Safety & quality: hallucination control, red-teaming
- Fine-tuning & adapters when needed
Backend & APIs
- Type-safe APIs, job queues, event-driven systems
- Streaming (SSE/WebSockets) for agent runtimes
- Auth, rate limits, multi-tenant architecture
- Integrations: tools, data sources, internal systems
Data & Storage
- Postgres, Redis, object storage, search indexes
- Data modeling for traceability & auditability
- Pipelines, scheduling, reproducibility
- Privacy-sensitive data handling
MLOps / Infra
- Kubernetes, containers, GPU workloads
- CI/CD for AI systems, environment parity
- Observability: traces, metrics, eval dashboards
- Cost control, scaling, reliability engineering
Projects & Labs
A connected ecosystem — built with a bias toward real constraints, research rigor, and long-term maintainability.
Arcyn
↗AI agent platform for scientists & R&D.
MyasoLab
↗Research lab: AI, space, complex systems.
Arctilux
↗AI agents & automation framework.
TerraAgent / Astra
↗Space & Earth data AI agents.
Custom client systems
↗Private builds (NDA).
Selected cases
Draft examples of the kinds of systems I ship: one “cover”, then two short paragraphs with context and outcome.
Research copilot with multi-agent orchestration
Designed a multi-agent workflow to turn ambiguous research questions into structured plans, tool calls, and verifiable outputs.
Focused on traceability, evaluation loops, and a runtime that stays debuggable under real complexity.
RAG system for high-stakes documentation
Built a retrieval pipeline (chunking, hybrid search, reranking) optimized for precision and citation discipline.
Added evaluation datasets and failure-mode analysis to keep answers grounded as the corpus grows.
Production LLM API with streaming UX
Implemented a type-safe API layer with SSE streaming, retries, and latency/cost controls for interactive agent runs.
Shipped observability (traces/metrics) so the system can be operated and improved continuously.
GPU inference + pipeline automation
Designed containerized GPU workloads and deployment patterns to keep model serving predictable and scalable.
Introduced CI/CD and environment parity to reduce “works on my machine” failures.
Knowledge graph + memory layer for agents
Integrated graph structures with retrieval to improve long-horizon reasoning and reduce context bloat.
Prioritized maintainable schemas and clear interfaces between storage, retrieval, and agent policies.
Evaluation harness for AI systems
Set up golden sets, benchmark suites, and regression checks for agent behaviors and RAG quality.
Made results actionable: dashboards, error clustering, and fast iteration loops for teams.
Automation assistant for ops-heavy workflows
Connected internal tools and data sources into a reliable automation layer with clear permissions and audit trails.
Reduced manual work while keeping safety controls for edge cases and exception paths.
AI media pipeline (generation → QA → delivery)
Prototyped a generation pipeline with deterministic steps, human-in-the-loop checks, and repeatable outputs.
Optimized for throughput and consistent quality rather than one-off demo results.
Deep-tech MVP to scalable architecture
Converted an early prototype into a modular architecture: clear boundaries, data contracts, and operational readiness.
Planned the path from MVP constraints to scale without a rewrite-first trap.
How I can help
I work with strong, non-trivial requests — systems that need clear architecture, real execution, and technical depth.
AI System Design
- Architecture
- Agent orchestration
- RAG / Graphs / Memory
R&D & Prototyping
- Fast experiments
- Feasibility validation
- MVPs for deep-tech
Custom AI Solutions
- Tailored systems
- Long-term collaboration
- Not off-the-shelf tools
I work on complex AI systems where research, engineering, and execution must align.
This includes turning uncertain ideas into structured architectures that can be built, tested, and deployed.
My work combines research thinking, engineering discipline, and entrepreneurial execution.
Schedule a call
Book a call to discuss your AI system, constraints, and technical direction. We’ll clarify whether the problem is solvable — and what it would take to build it properly.
Let’s work together
This is a good fit for problems that require both research depth and production-grade engineering.