Senior Software Engineer · Forward Deployed Engineering · Seattle
I bridge customers, product needs, and engineering, turning ambiguous problems into scalable AWS systems, production software, and AI/ML solutions.
Distributed Systems
Reliable platforms emerge from explicit trade-offs across ingestion, storage, querying, observability, and recovery.
Applied AI
Production-minded AI
I approach AI as an engineering system: understand the user problem first, then design retrieval, embeddings, context, evaluation, model serving, latency, observability, and failure handling around the actual workflow.
System view
AI products should be treated as systems: quality, cost, latency, grounding, and reliability need to be evaluated together. I work across Python, PyTorch, TensorFlow, transformer-based NLP, RAG, and retrieval/evaluation workflows.
Projects
Cloud Systems + Deep Learning
Built an image-recognition service on AWS for workloads where deep-learning inference can create unpredictable processing demand. The architecture uses separate web and app tiers, SQS request and response queues, S3 for durable input and output storage, and custom EC2 images for inference workers. Capacity scales with queue depth, creating or terminating app-tier instances as demand changes.
Machine Learning
Implemented an unsupervised learning approach to estimate a user's Big Five personality characteristics from social-media traces such as likes and comments. The project explores how behavioral signals can be transformed into features for personality-oriented classification.
Full Stack + Location-Aware Workflow
Designed a patient-to-doctor dispatch workflow with separate patient and physician roles. A patient submits a request with symptoms, geo queries identify nearby available doctors, and once a physician accepts, the patient's details and location are delivered while competing requests are closed. Dashboards track doctor and patient assignment activity.
Data Engineering + Visualization
Built an interactive analysis for the VAST Challenge 2010 pandemic dataset. Python preprocessing normalizes noisy symptom terminology, FP-Growth and association rules identify syndrome relationships, and D3 visualizations expose pandemic spread across countries, age groups, syndrome criticality, gender-based mortality, and life-versus-death rates.
Experience
Senior Software Engineer
Software Development Engineer Intern
Senior Software Engineer
Research Assistant · AI/ML
Software Engineer
Education
Master’s in Computer Science
B.Tech in Computer Science
About
I’m Nagasai Kallakuri, a Senior Software Engineer based in the Seattle area. I work best where the problem is ambiguous, the system is complex, and strong engineering judgment matters, the same environment that defines Forward Deployed Engineering, senior IC, and technical lead roles.
My engineering path spans customer-facing web applications, public-safety software, backend services, cloud-native platforms, distributed data systems, and AI/ML. I’ve worked across React, Java, Node.js, SQL, AWS, streaming, search, Python, PyTorch, and TensorFlow, from problem discovery through architecture, implementation, deployment, and production ownership.
I’m most interested in problems where software architecture, reliability, product usability, and intelligent systems intersect. I value explicit trade-offs, measurable outcomes, clear ownership, and systems that remain understandable as they grow.
Role Focus
Customer problem → production system
Architecture and execution
Direction, mentorship, and delivery
Engineering philosophy
Start with the real problem. Understand the constraints. Make the trade-offs explicit. Ship something useful, measure it in production, and improve it with the customer.