Vetted Software Engineers in the Greater Boston

Sahil Gupta

Screened ReferencesStrong rec.

Junior AI Software Engineer specializing in LLM agents, RAG, and healthcare NLP

MA, U.S.A1y exp

“Backend engineer who built an agentic LLM system for private equity/finance that answers questions over enterprise contracts and documents using a vector-db RAG pipeline. Differentiator is a trust-focused citation framework (with highlighted source text) to reduce hallucinations in high-stakes workflows, plus strong DevOps experience deploying microservices on Kubernetes with Helm/GitOps and building Kafka real-time pipelines.”

Priyanka Ramesh

Screened ReferencesStrong rec.

Junior Full-Stack Software Engineer specializing in Java/Spring Boot and React

Boston, MA2y exp

“Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”

“Full-stack AI engineer who built Skillmatch AI, an LLM/RAG-based job matching platform using FastAPI microservices, Airflow-orchestrated async pipelines, and Pinecone vector search (sub-second retrieval across 50k+ vectors) deployed on GCP with autoscaling. Also partnered directly with a cancer researcher to automate SEER + PubMed-driven report generation via an AI pipeline, emphasizing rapid prototyping and outcome-focused communication.”

Rasega Babu Kavitha

Screened

Mid-Level Full-Stack Software Engineer specializing in Java/Spring and React

Boston, MA4y exp

“Full-stack engineer who has shipped a real-time social engagement feature (live messaging + personalized feeds) for a career networking platform, owning everything from WebSockets/SSE and JWT+Redis auth through Docker/Kubernetes production deployment. Also built a production Flask backend for an AI-driven movie recommendation system on AWS, with strong API design (versioning/error standards) and hands-on performance tuning (Typesense +47% query improvement, Postgres indexing, Redis caching, CloudWatch-driven incident response).”

“Robotics simulation/localization engineer who built a lunar crater navigation stack in ROS/ROS2 and Gazebo, including custom localization/perception/planning packages. Demonstrated strong debugging skills by using tf2 frame analysis to fix camera-to-base_link alignment, cutting heading error from 75° to 0.48°, and handled large NASA lunar imagery (~4GB) by converting/downsampling data for Gazebo.”

“At Unar Labs, built and operationalized LLM capabilities inside a cloud-native SaaS product, emphasizing production reliability (fallbacks, observability, cost/latency/quality monitoring) and iterative improvement from user feedback. Also acts as a customer-facing technical lead—running developer demos/workshops and supporting sales through discovery, pilots/POCs, and technical walkthroughs to drive production adoption.”

“Backend/cloud engineer with hands-on ownership of a climate data extraction pipeline (BeautifulSoup + Pandas ETL + CRON) that automated 50k+ monthly data points and removed ~20 hours/week of manual work. Also built a multi-AZ Kubernetes deployment for a Node.js system using Terraform and GitHub Actions (blue-green, rollbacks) and has Kafka/FastAPI experience from a healthcare plan management project.”

Anudeep Reddy Veerla

Screened

Mid-Level Software Engineer specializing in cloud-native microservices

Boston, MA3y exp

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”

Shiva Dasi

Screened

Mid-level Software Engineer specializing in LLM agents and cloud-native systems

Boston, MA4y exp

“Built and shipped production LLM agents in compliance-sensitive environments (FERPA), emphasizing reliability via structured outputs, state-graph orchestration (LangGraph), and CI-driven eval/regression testing. Also has experience hardening messy ERP ingestion pipelines at scale (50K monthly orders) with normalization, idempotency/deduplication, and robust failure handling using AWS (SQS/CloudWatch) and PostgreSQL.”

“Frontend engineer with experience modernizing a large healthcare platform from Adobe Flex to React/TypeScript using micro frontends while keeping legacy systems live during migration. Brings strong performance tuning skills for data-heavy UIs and real-time map applications, including profiling-driven React optimization, Next.js SSR, and WebSocket-based live tracking.”