Services

How I usually contribute when a team already has real delivery pressure.

I work best close to product, data, and backend teams that need someone to connect applied AI ideas to stable systems and operational decisions.

Embed In Delivery

Best fit when a team needs a senior engineer inside a live delivery cycle, shaping architecture, APIs, and operational decisions instead of producing abstract AI strategy.

Turn institutional requirements into practical technical scope
Work across product, data, backend, and release boundaries
Keep delivery grounded in user adoption and operational constraints

Design Applied AI Systems

Hands-on work for retrieval, NLP, evaluation, and human-in-the-loop systems that need to behave well beyond demos.

Define system boundaries for LLM and ML workflows
Translate model behavior into stable APIs and operator-facing tools
Make failure modes, feedback loops, and rollout tradeoffs explicit

Modernize Model Operations

Platform and MLOps work for teams that already have models or services in motion and need them to be safer, faster, and easier to maintain.

Standardize retraining, packaging, and release paths
Improve traceability with MLflow, DVC, and orchestration tooling
Reduce dependency on hidden manual steps and tribal knowledge

Document Intelligence Pipelines

Implementation of applied AI pipelines that transform PDFs and unstructured documents into reliable structured outputs for downstream systems.

Design RAG and extraction workflows for document-heavy use cases
Convert PDF metadata and text into schema-driven structured records
Connect retrieval pipelines to vector stores and production APIs

Production LLM Evaluation

Quality and release-readiness systems for teams that need measurable confidence before shipping prompt, model, or workflow changes.

Build evaluation pipelines for hallucination and factuality monitoring
Compare versions using ground-truth and LLM-as-a-judge assessments
Define practical quality gates for safer production releases

Agent Orchestration and Integration

Scalable orchestration for long-running AI workflows, with queue workers and service boundaries designed for production reliability.

Implement worker-based orchestration with Kafka and API services
Standardize model-provider access through LiteLLM layers
Integrate AI workflows into existing microservice environments

Next step

The clearest proof is still the work itself.

If you are evaluating fit, start with the case studies. They show the level of ownership, system design, and delivery judgment behind these collaboration modes.

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