ML and AI engineering
Supervised learning, forecasting, evaluation pipelines, retrieval workflows, and LLM-assisted systems that need rigor beyond demo-level output.
FREELANCE AND CONTRACT WORK
Bring me in for high-stakes AI and systems delivery.
I take on focused freelance work where the technical bar is real: model pipelines, scientific analysis systems, forecasting, data engineering, research prototyping, and product features that need to move from concept to dependable implementation.
SERVICE MENU
Freelance services
Scientific and research tooling
Reproducible experiment pipelines, data processing, benchmarking, automation, and reporting for technically demanding research environments.
Product and backend implementation
APIs, orchestration, analytics dashboards, and supporting systems around AI applications that need to be stable enough for real users and stakeholders.
Technical due diligence
Codebase review, architecture critique, model workflow risk analysis, and practical recommendations that a founder or team can actually execute.
ENGAGEMENT MODEL
How I usually work
1 to 2 weeks
Best for audits, prototypes, proof-of-concept builds, or unblocking a stuck engineering effort.
3 to 6 weeks
Best for scoped feature delivery, pipeline rebuilds, experimental systems, or analytics products.
Ongoing support
Best for teams that need a senior technical partner across architecture, experimentation, and delivery.
WHAT YOU GET
Freelance output expectations
Deliverables
- Clear scope and assumptions before implementation begins
- Pragmatic architecture decisions tied to the actual business problem
- Readable code and repeatable workflows instead of fragile hacks
- Documented handoff notes so the work remains usable after delivery
Best clients for this setup
- Founders who need an experienced technical builder fast
- Research groups lacking engineering bandwidth
- Small teams modernizing a legacy model or data pipeline
- Product teams validating an AI feature before a larger hire
CONTACT
Request a freelance quote
Include these details if possible
- Project objective and current blockers
- Data sources, stack, or model requirements
- Budget range and expected deadline
- Whether you need strategy, implementation, or both