Ruslan Shulga

IbuildproductionAI platformsfortheenterprise.

VP Engineering at JPMorgan Chase. The systems I lead serve several thousand employees every day. Multi-agent orchestration, hybrid retrieval, custom MCP servers.

About

Nine years in production. Four shipping enterprise AI.

For the last four years I've led AI platform engineering at JPMorgan Chase. My team built the multi-agent orchestration layer that several thousand employees use daily. We also wrote the hybrid RAG pipeline behind internal search and the MCP servers that connect Claude to internal APIs.

Before JPMC I spent two years on React and Next.js work at Earth Designs and two years running IT operations at PPS Capital. Nine years of production engineering total, four of them shipping AI in a regulated enterprise.

My focus right now is the boring infrastructure under agentic AI: evaluation pipelines that catch regressions before users notice, agents that fail safely when the model misfires. The stuff that rarely gets demoed but always decides whether the system holds up in production. I'm looking for what comes next.

Selected Work

  1. 01

    Multi-agent orchestration platform

    Production multi-agent workflows used by several thousand employees firm-wide. LangGraph orchestrates Claude Agent SDK and OpenAI Agents SDK. Each agent has persistent memory and calls internal tools. Irreversible actions pause for a human checkpoint.

    LangGraph Claude Agent SDK OpenAI Agents SDK Python React
    Manual processing cut on flagship workflow
    ~55%
    Concurrent users at peak
    several thousand
    Read case study →
  2. 02

    Hybrid retrieval pipeline

    Replaced an embedding-only RAG with a hybrid pipeline: dense plus sparse retrieval, cross-encoder re-ranking, indexed across Pinecone, Weaviate, and pgvector depending on the use case. The pieces that mattered weren't the models. They were the eval harness and the index strategy per domain.

    Pinecone Weaviate pgvector Cross-encoder re-ranking RAGAS
    Retrieval precision improvement
    ~35%
    Eval cadence
    nightly
    Read case study →
  3. 03

    Internal MCP servers

    Custom Model Context Protocol servers that connect Claude to internal APIs and databases. Around 8 product teams use the stack. Features that used to take a sprint now take a day or two.

    MCP Python Anthropic API Internal APIs
    Teams using the stack
    ~8
    Typical feature ship time
    1-2 days vs 1 sprint
    Read case study →
  4. 04

    Model-agnostic AI gateway

    An AI gateway on top of AWS Bedrock and Azure OpenAI, with direct Anthropic API access for newer Claude models. Around 8 product teams route through it for model selection, automatic failover, and centralized cost tracking across Claude, GPT-4, Llama, and Titan.

    AWS Bedrock Azure OpenAI Anthropic API Cost tracking Provider failover
    Product teams routing through
    ~8
    Providers supported
    3 primary, 4+ models
    Read case study →
  5. 05

    Multimodal document pipelines

    Document and image processing pipelines on Claude Vision and GPT-4V, with Gemini added recently for cost reasons. Handles the first pass of compliance reviews that used to sit with analysts.

    Claude Vision GPT-4V Gemini OCR Compliance review automation
    Use case
    First-pass compliance review
    Originally done by
    Human analysts
    Read case study →

Contact

Working on something interesting? Let's talk.

Location
New York, NY