I am a software engineer focused on designing, building, and shipping production-grade systems that hold up under real traffic, real data, and real users. My work sits at the intersection of distributed backend engineering, applied AI/ML, and full stack product development — architecting services that stay correct at scale, training and deploying models that create measurable business value, and delivering interfaces people actually want to use.

I approach engineering with a product mindset: clean abstractions over clever code, measurable outcomes over feature counts, observability and security designed in from day one rather than retrofitted. I care about latency budgets, cost per request, deployment safety, and the long-term maintainability of everything I put my name on.

Core Focus

• Software Engineering — distributed systems, API design, event-driven architecture, performance engineering, clean architecture, and test-driven delivery
• AI / ML Engineering — deep learning, NLP, computer vision, LLM systems, RAG pipelines, model deployment, and MLOps lifecycle ownership
• Full Stack Development — type-safe frontends, high-throughput backends, relational and vector data modeling, real-time systems
• Product Engineering — requirement decomposition, technical trade-off analysis, cross-functional delivery, and shipping iteratively with instrumentation

Open To

Software Engineering Roles · AI / ML Engineering Roles · Full Stack Development Roles · Backend & Platform Engineering · Research Collaborations · Open Source Contribution

Domain	Proficiency	Details
Deep Learning		CNNs, RNNs, LSTMs, Transformers, attention mechanisms, transfer learning, custom loss design, distributed multi-GPU training
Natural Language Processing		Tokenization pipelines, embeddings, NER, summarization, sentiment analysis, sequence-to-sequence modeling, transformer fine-tuning
Large Language Models		Prompt engineering, RAG architecture, LoRA / PEFT fine-tuning, function calling, agentic workflows, evaluation harnesses, guardrails
Computer Vision		Object detection, semantic segmentation, OCR pipelines, image classification, augmentation strategy, real-time inference
MLOps & Deployment		Model registry, CI/CD for ML, containerized inference, autoscaling GPU serving, drift detection, experiment tracking
Vector Search & Retrieval		Embedding stores, hybrid search, HNSW / IVF indexing, chunking strategy, reranking, semantic caching
Data Engineering		ETL orchestration, streaming ingestion, feature stores, data validation, schema evolution, warehouse modeling
Classical Machine Learning		Ensemble methods, gradient boosting, dimensionality reduction, clustering, feature engineering, hyperparameter optimization
MLOps Observability		Latency and cost telemetry, A/B rollout of models, shadow deployment, quality regression dashboards

Two-sentence professional summary of the role: the team, the domain, the systems you owned, and the business outcome you were accountable for.

Scope of Work

• Designed and shipped FEATURE_OR_SERVICE, serving XX,XXX daily requests with pXX latency under XXX ms
• Reduced METRIC by XX% by REPLACING_OR_OPTIMIZING specific component, validated through load testing and production telemetry
• Owned end-to-end delivery of MODULE across design review, implementation, testing, deployment, and on-call support
• Improved test coverage from XX% to XX% and cut CI pipeline duration by XX% through parallelization and caching
• Collaborated with product, design, and data teams to decompose ambiguous requirements into shippable increments

Skills

Two-sentence professional summary of the role: the modeling problem, the data you worked with, and the measurable improvement you delivered to production.

Scope of Work

• Built and deployed MODEL_OR_PIPELINE, improving BASELINE_METRIC from XX% to XX% on held-out evaluation data
• Engineered the training and inference pipeline end to end, including data validation, versioning, and reproducible experiment tracking
• Cut inference cost per request by XX% via quantization, batching, and caching without measurable quality regression
• Authored the evaluation framework used to gate model releases, covering accuracy, latency, and failure-mode regression suites
• Presented findings and trade-offs to engineering and non-technical stakeholders to drive rollout decisions

Skills

Two-sentence summary of your open source involvement: the projects, the nature of your contributions, and their reach.

Scope of Work

• Merged XX pull requests across AREA_ONE and AREA_TWO, including performance fixes and API ergonomics improvements
• Triaged and resolved XX issues, reducing maintainer load and improving first-response time for new contributors
• Authored documentation and examples that lowered the onboarding barrier for downstream consumers of the library

Skills

learning:
  - Distributed systems design and consensus protocols
  - Advanced LLM system architecture and agentic orchestration
  - Rust for performance-critical and memory-safe services
  - Kubernetes operators and platform engineering patterns

building:
  - PROJECT_ONE_NAME  # production system at XX,XXX daily requests
  - PROJECT_TWO_NAME  # retrieval-augmented ML pipeline
  - Open source contributions to PROJECT_OR_ORG_NAME

exploring:
  - Vector database internals and approximate nearest neighbor indexing
  - Cost-aware inference optimization and model quantization
  - Event-driven architecture at scale with exactly-once semantics
  - Developer experience tooling and internal platform design

open_to:
  - Software Engineering roles
  - AI / ML Engineering roles
  - Full Stack Development roles
  - Backend and Platform Engineering roles
  - Research collaborations and open source maintainership

Engineering is not about writing code — it is about designing systems that outlive the assumptions they were built on.