AI & RAG
Build source-aware AI tools that retrieve useful context before generating an answer.
- Document Q&A and knowledge assistants
- Semantic search and retrieval pipelines
- Grounded LLM responses
- AI research and internal knowledge tools
Available for AI/ML engineering · freelance · contract opportunities
Parmod
AI/ML Engineer
I build reliable AI applications, product analytics systems, ML APIs and production-oriented ML infrastructure.
My work moves beyond notebooks into tested APIs, applications, data systems, deployment, monitoring and documented engineering decisions.
4Flagship Engineering Systems
3OCI AI/ML Credentials
4Technical Internships
What I can build
Focused AI/ML Engineering Services
Practical systems designed around a defined problem, inspectable evidence and a maintainable delivery path.
Product Analytics
Turn product events into usable retention, funnel, churn, cohort and experimentation evidence.
- DAU, WAU, MAU, funnels and cohorts
- Retention and churn intelligence
- Customer segmentation
- Experimentation and product dashboards
ML APIs & MLOps
Move a model beyond a notebook into a validated API with release, monitoring and rollback controls.
- FastAPI inference services
- Data and artifact validation
- Drift and model monitoring
- CI/CD, staged release and rollback
Cloud & Reliability
Build and strengthen Python services with explicit reliability, observability and cloud-delivery boundaries.
- Python and FastAPI services
- PostgreSQL, Redis and asynchronous backends
- Docker, monitoring and observability
- Kubernetes, Terraform, AWS and GCP delivery
Selected engineering work
Systems With Inspectable Engineering Evidence
Four portfolio systems spanning product intelligence, distributed reliability, ML governance and evidence-grounded AI research.
Versioned release
Product intelligence platform
RetentionOS
Analytics · Experimentation · Churn ML · Platform Engineering
Problem fit: SaaS analytics, retention intelligence, churn analysis, experimentation and internal product-data tooling.
System
A reproducible product-intelligence platform joining behavioral events, SQL analytics, retention and funnel analysis, calibrated churn prediction and governed A/B testing.
Strongest proofLive end-to-end workflow, v1.0.0 release and 167 backend tests passed
10KSynthetic users
~238KProduct events
167 passedBackend tests
v1.0.0Release
Versioned release
Reliable infrastructure for AI/ML services
Resilient Distributed Runtime
Distributed Systems · Reliability · Kubernetes · AWS
Problem fit: backend reliability, observability, container/cloud delivery, distributed coordination and resilient AI/ML infrastructure.
System
Correctness-first infrastructure combining bounded execution, cluster membership, causal CRDT replication, durable recovery, mTLS, observability, chaos testing and reproducible delivery.
Strongest proofThree-node Kubernetes verification with failure recovery and 439 tests passed
439 passedAutomated tests
3 nodesLocal cluster
v0.7.0Release
EKS verifiedCloud lifecycle
Versioned release
Synthetic ML governance case study
CreditScoreV4 ML Governance
Model Risk · Responsible AI · MLOps · Release Safety
Problem fit: ML serving, monitoring, data quality, drift detection, governed releases and production-readiness reviews.
System
A deterministic governance system testing whether unsafe data and model changes are blocked through independent quality, drift, fairness, integrity, promotion, rollback and incident controls.
Strongest proofTagged v1.0.1 release with independent fail-closed controls
v1.0.1Release
Fail-closedGovernance
Canary + rollbackRollout
AWS gatedDeployment
Versioned release
AI-assisted portfolio research system
AXIOM Portfolio Intelligence
Constrained MPT · FinBERT · FAISS · Evidence-grounded RAG
Problem fit: quantitative research platforms combining analytical computation, document retrieval and source-grounded AI explanations.
System
A research system that evaluates constrained portfolio strategies and produces source-grounded explanations without allowing generated text to change allocations.
Strongest proofEqual weighting outperformed the optimized strategy on return and Sharpe in the documented walk-forward evaluation
84Automated tests
v1.0.0Release
Walk-forwardEvaluation
Equal weightStronger baseline
Temporary research endpoint; not a production financial service.
Two paths
Hiring for an AI/ML engineer?
Review experience, education, technical strengths and selected systems in a recruiter-friendly format.
View ResumeNeed help building an AI/ML system?
See the focused problems I can help solve and the portfolio evidence that demonstrates each capability.
Explore ServicesHow I work
From an Unclear Problem to a Reviewable System
- Understand the problem
- Define the smallest useful architecture
- Build and test
- Deploy and observe
- Document and handoff
Start a conversation
Have an AI/ML problem worth solving?
Recruiters, founders, engineering teams and collaborators are welcome to share the role, system, or technical problem they are working through.