Vetted Shell Scripting Professionals

“End-to-end engineer focused on AI-native enterprise systems, including a production generative knowledge platform using RAG + semantic search over internal documentation (React, Python/Flask, GPU-hosted NLP models, Pinecone) with strong CI/CD and observability. Reports concrete outcomes including 40% faster knowledge access and ~75% employee adoption, and has led incremental cloud-native modernization using feature flags, parallel runs, canary releases, and regression testing.”

“Backend-focused Python engineer who owned high-traffic internal services end-to-end (FastAPI/Django) including REST/GraphQL APIs, PostgreSQL optimization, async task processing via SQS, and full CI/CD. Strong Kubernetes-on-EKS and GitOps (ArgoCD + Helm) experience, plus Kafka real-time streaming work and phased cloud-to-on-prem migration support.”

LAKSHMI KATTAMURI

Screened

Mid-level Embedded Software Engineer specializing in RTOS and microcontroller firmware

Boston, MA4y exp

“Backend/embedded-focused engineer with hands-on experience designing real-time, memory/power-constrained firmware architectures and also building Python/FastAPI services. Demonstrates strong production rigor across migrations (strangler pattern, shadow reads, feature flags), security (OIDC/JWT, RBAC/ABAC, Postgres RLS), and robustness for async workflows (idempotency, event ordering, state machines).”

“Healthcare-focused ML/AI engineer who has built clinical note summarization and medical image annotation solutions using LLMs, RAG, and multimodal models. They combine experimentation across major model providers with practical production concerns like monitoring, drift detection, and latency/cost tradeoffs, and also earned 2nd place in a Google hackathon for a medical AI assistant.”

“New grad candidate with graduate research experience building a multi-agent RAG pipeline from scratch, including worker-coach orchestration and an evaluation framework. Most notably, they improved structural similarity from 67% to 98% by designing validation and retry logic to reduce hallucinations, showing strong practical depth in agentic AI systems.”

“Backend engineer with fintech/AI startup experience who built an Azure serverless, event-driven pipeline for large-scale crypto sentiment analysis and semantic search (OCR/NLP to vector search) and integrated LLM + blockchain data for predictive insights. Demonstrated measurable impact (25% lower retrieval latency, 10% fewer data errors, 15% higher engagement) and has led safe microservices migrations with strong security and reliability practices.”

“Built and productionized a clinical NLP + patient risk stratification platform at Dermanture, combining Spark/PySpark pipelines with BERT/BioBERT for entity extraction and text classification and downstream risk models in TensorFlow/scikit-learn. Experienced running regulated, auditable ML workflows with Airflow and AWS SageMaker, emphasizing data validation (Great Expectations), drift monitoring, and explainability (SHAP) to drive clinician trust and adoption.”

“Accenture engineer who built and shipped a production RAG-based automation/chatbot for SAP incident triage and troubleshooting, embedding thousands of runbooks/logs/tickets into a semantic search pipeline and integrating it into Teams/Slack. Reported major productivity gains (30–60% time reduction), >90% validated answer accuracy, and sub-2-second responses, with strong orchestration (Airflow/Prefect/LangGraph) and reliability practices (guardrails, testing, monitoring).”

“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.”

“LLM/RAG-focused engineer who repeatedly takes agentic workflows from impressive demos to dependable production using rigorous evals, SLOs, and deep observability. Has led high-impact incident mitigation (22-minute MTTR during a major sale) and developer enablement workshops, and partnered with sales to close a $410k ARR enterprise deal with a tailored RAG pilot (FastAPI/pgvector/Okta/InfoSec-ready).”

“ML/NLP engineer with banking domain experience who built a GenAI-powered fraud detection and risk intelligence system at Origin Bank, combining RAG (LangChain + FAISS), fine-tuned BERT NER, and GPT-4/Sentence-BERT embeddings. Delivered measurable impact (25% higher fraud detection accuracy, 40% less manual review) and emphasizes production-grade pipelines on AWS SageMaker/Airflow with strong data validation and scalable PySpark processing.”

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).”

Akshay Bharadwaj Kunigal Harish

Screened

Mid-level Machine Learning Engineer specializing in NLP, computer vision, and LLM systems

Boston, MA5y exp

“Built a production multi-agent cybersecurity defense simulator orchestrated with CrewAI, combining Red/Blue team LLM agents, a RAG runbook retriever, and an RL remediation agent trained via state-space simplification and reward shaping for rapid incident response. Also partnered with quant analysts and fund managers to deliver an automated trading and portfolio management system using statistical methods plus CNN/LSTM models, reporting up to 15% weekly ROI.”

Mahiyadav Sidda

Screened

Junior Machine Learning Engineer specializing in LLMs, RAG, and on-device AI

Bangalore, India2y exp

“Built an "Offline Study Assistant" that runs LLM inference locally on a 5-year-old Android device using Llama.cpp and the Android NDK, achieving a 27x speedup and cutting time-to-first-token from 11 minutes to 30 seconds. Also has applied backend/API experience with FastAPI, Supabase (Auth + RLS), and production hardening of a RAG system at Hashmint using Celery and Redis to eliminate PDF-processing-related query failures.”

“Frontend engineer focused on high-scale React + TypeScript dashboards, including an internal Instagram creator/agency analytics dashboard handling extremely large datasets (1–2TB) with virtualization and performance profiling to maintain ~60fps UX. Experienced in modern state management (Redux Toolkit/RTK Query), modularizing legacy codebases into shared component libraries (Storybook), and shipping fast with feature flags plus automated QA (Playwright/Selenium).”

“ML/computer-vision practitioner who shipped a CycleGAN-based bilingual handwriting translation demo (English↔Telugu) for low-resource scripts using unpaired datasets, focusing on preserving handwriting style and real-time deployment via Gradio. Also delivered a medical imaging pipeline by fine-tuning ResNet-50 and ViT-B/16 for pneumonia detection, emphasizing reproducibility, measurable evaluation, and stakeholder-friendly iteration.”

“Software engineer experienced delivering customer-facing, real-time industrial monitoring dashboards (motors/shafts/turbines) by partnering directly with end users to refine charts, alerts, and performance. Strong in API/platform integrations and production troubleshooting—uses feature flags, logging, validation/mapping, containerization, and performance testing to keep systems stable while iterating quickly.”

Harsh Chauhan

Screened

Junior AI Engineer specializing in Generative AI, RAG, and NLP

Remote, US3y exp

“AI/LLM engineer who has shipped a production RAG platform at Ticker Inc. on GCP (Qdrant + Postgres) delivering sub-second retrieval over 550k+ items, with measurable gains in latency and answer quality (HNSW optimization, MMR re-ranking). Also built an asynchronous LangChain/LangGraph multi-agent research system (10x faster cycles) and partnered with Indiana University doctors on synthetic patient records and ML error analysis using clinician-friendly F1/loss dashboards.”

Purva Chakravarti

Screened

Senior Full-Stack Software Engineer specializing in scalable web apps, cloud, and blockchain/AI

Chino, California11y exp

“Full-stack engineer with strong production ownership across React/TypeScript, Node.js, and AWS (EC2/ECS/RDS/CloudWatch), including CI/CD, observability, and incident response. Delivered a secure RBAC workflow module end-to-end and achieved measurable gains (~30–40% latency reduction, ~50% error reduction) that lowered infra/ops costs. Comfortable in high-ambiguity startup environments—shipped a payment module within 2 days of joining with no documentation.”

Yash Prakashbhai Shah

Screened

Senior Backend Software Engineer specializing in Java, microservices, and cloud infrastructure

Santa Clara, CA6y exp

“Backend/platform engineer at Aryaka Networks who built a centralized resiliency and security Spring Boot library to standardize Keycloak RBAC and fault-tolerance across 25+ Kubernetes-migrated microservices. Uses profiling and observability (Prometheus/Grafana) to drive measurable performance and reliability gains (25% faster APIs, 70% faster environment setup) and accelerates adoption via golden-path starter repos and Swagger/OpenAPI live docs.”

“AIX/IBM Power Systems engineer with hands-on production performance troubleshooting (NMON) and operational administration across VIOS/HMC/LPAR/DLPAR. Has implemented enterprise patching and security remediation using NIM (e.g., OpenSSH vulnerability) and built automation to generate and curate weekly AIX images for the NIM environment.”

“Data/ML engineer from HCLTech who modernized enterprise data by linking fragmented financial and supply-chain data across SAP/SQL Server/Snowflake using NLP entity linking and embeddings (FAISS). Delivered measurable impact including ~40% reduction in manual error-log triage and entity-linking accuracy improvements from ~86% to ~93%, with results surfaced in Power BI for real-time analytics.”

“Cloud-focused application security practitioner with hands-on AWS and Kubernetes experience, including securing a manufacturing monitoring platform (API auth, least-privilege IAM, CI/CD security checks) and troubleshooting a production data-ingestion outage caused by an overly restrictive IAM change. Experienced in implementing cloud-native security tooling (IAM Access Analyzer, Inspector, CloudWatch) and deploying monitoring/security agents via Kubernetes sidecars with Helm, Prometheus/Grafana, and Jenkins-driven CI/CD.”

“Cloud/infrastructure engineer with strong production operations background across AWS, Azure, and Kubernetes, supporting 30+ enterprise workloads for ~40,000 users. Demonstrated incident leadership (hybrid AKS-to-AWS routing outage) with a reported 60% MTTR reduction, plus hands-on CI/CD (Jenkins) and Terraform-based IaC for AWS (VPC/EC2/EKS). Lacks direct IBM Power/AIX/PowerHA experience but emphasizes transferable ops and troubleshooting skills.”