Vetted Containerization Professionals

Srikanth Reddy

Screened

Mid-level AI/ML Engineer specializing in GenAI and financial risk & compliance analytics

Plainsboro, NJ7y exp

“Built and deployed a production LLM-powered financial risk and compliance platform to reduce manual trade exception handling and speed up insights from regulatory documents. Implemented a LangChain multi-agent workflow with structured/unstructured data integration (Redshift + vector DB) and emphasized hallucination reduction for regulatory safety using Amazon Bedrock. Strong MLOps/orchestration background across Kubernetes, Airflow, Jenkins, and monitoring/testing with MLflow, Evidently AI, and PyTest.”

Aishwarya Thorat

Screened

Intern Data Scientist specializing in ML engineering and LLM agentic workflows

San Francisco, CA6y exp

“Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.”

“Full-stack engineer who described building Matrix, a meeting-room booking system across React, ASP.NET Core, and SQL Server. Stands out for owning the workflow end-to-end, emphasizing backend-enforced booking conflict validation, React performance optimization, and integrations like Google Calendar and email notifications.”

“Backend engineer in airport operations who built a highly customizable BFF-based system connecting airport staff workflows to a baggage sortation engine. Their architecture cut per-airport customization from 100-150 engineering hours to 1-5 hours, improved long-running operation performance by 45%, and shipped in 4 months instead of 6. They also explored AI-assisted backend customization with human validation and test-based safeguards.”

“Backend/platform engineer with experience across Cigna, Cognizant, and a university environment, focused on reliability, distributed systems, and regulated-domain workflows. Stands out for combining Kubernetes/Kafka/AWS infrastructure expertise with a production RAG-based healthcare compliance assistant that cut manual reporting work from 30-45 minutes to under 2 minutes while maintaining strong uptime and data-quality controls.”

Lawrence Prabhakar

Screened

Mid-level Full-Stack Java Developer specializing in FinTech

USA5y exp

“Engineer with a thoughtful, hands-on approach to AI-assisted software development, treating AI as a force multiplier for debugging, prototyping, and large-codebase work rather than a substitute for judgment. Particularly strong in multi-agent coding workflows, contract-driven development, and maintaining consistency across backend, frontend, and testing through shared schemas and OpenAPI-based coordination.”

“Full-stack engineer with experience spanning AI-powered product features and healthcare fraud detection systems. Has built end-to-end LLM-enabled applications, customer-facing recommendation systems at scale, and operational platforms that improved real-time investigations and flagged over 1,200 high-risk cases quarterly.”

“Full-stack and AI product engineer with strong AWS/Snowflake experience who built an internal feature flag platform and helped migrate a cybersecurity insights product into a multi-agent AI chat interface. They report production scale of 1M+ embeddings and 50k+ monthly queries, with outcomes including an 80% reduction in analyst work and dashboard generation in 7 minutes; the work was also featured by Claude and AWS.”

“Early-career/new-grad candidate who built TrendScout AI, an evidence-first market intelligence agent that ingests messy news, extracts entities/events, builds a Neo4j knowledge graph, and answers questions via RAG with citations. Achieved ~95% retrieval relevance by combining ChromaDB semantic search with graph-based retrieval and validating outputs through human evaluation and guardrails to prevent hallucinations.”

Shreya Kamath

Screened

Junior Full-Stack Engineer specializing in AI-powered web platforms

San Francisco, CA2y exp

“Full-stack engineer with a strong blend of fintech and applied AI experience, having built a Stripe-based payments platform end to end and shipped an AI meeting intelligence system using LiveKit, Deepgram, and OpenAI. Particularly compelling for teams needing someone who can design reliable backend systems, productionize LLM features, and operate effectively in early-stage, ambiguous environments.”

“Backend engineer who optimized an AI-driven portfolio analytics/insights platform at Fidelity, addressing latency and traffic growth by moving services toward microservices, improving service communication, and tuning API/DB performance. Experienced scaling Python/FastAPI services with Docker + Kubernetes autoscaling, and strengthening security/privacy for sensitive client portfolio data used in LLM-based reporting.”

“Robotics-focused engineer with hands-on projects ranging from a solo Dobot Magician Lite tic-tac-toe system (computer vision + minimax) to integrating an LLM with a Dobot arm for real-time pick-and-place via structured action outputs and validation. Also brings prior full-time DevOps experience (Docker/Kubernetes and CI/CD) and has used ROS/Gazebo for simulation work, including exploring improvements to crowd-aware navigation using human-trajectory datasets.”

“ML/AI engineer with healthcare payer experience (Signal Healthcare, Cigna) who has shipped production fraud/claims prediction systems using Python/TensorFlow and exposed them via FastAPI/Flask microservices integrated with EHR and Salesforce. Emphasizes operational reliability and trust—Airflow-orchestrated pipelines with data quality gates plus SHAP-based interpretability, A/B testing, and drift/debug workflows—backed by reported outcomes of 22% lower false payouts and 17% higher model accuracy.”

“Backend/ML engineer who built an end-to-end greenwashing detection system for corporate ESG reports: Python preprocessing pipeline, logistic regression + fine-tuned DistilBERT models, and a Dockerized FastAPI inference service optimized for latency. Internship experience maintaining GitLab CI/CD for TypeScript services (Jest/Playwright), improving pipeline stability and test determinism; familiar with Kubernetes/GitOps concepts and AWS CLI/SSO.”

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“Backend-leaning full-stack engineer from ADP’s Global View team who owned major backend components of an enterprise payroll dashboard, including a fault-tolerant multi-step payroll processing workflow and error visibility features. Strong in Java/Spring Boot + PostgreSQL schema design and Redis caching, with additional production experience in Python services (JWT, testing, SonarQube) and AWS deployments via Terraform/Jenkins with autoscaling.”

“Backend/AI engineer who has built production Spring Boot APIs on AWS (JWT auth, Redis/MySQL) and solved a real-world silent data integrity issue by implementing idempotency keys plus DB constraints/transactions. Also shipped an LLM-based document Q&A feature using a RAG pipeline with evaluation + human review, and designed multi-step agent workflows with verification, retries, and escalation guardrails.”

“DevOps/Cloud engineer with experience supporting large-scale enterprise infrastructure (AT&T: 50+ Power8/Power9 frames and 2,000+ AIX 7.1/7.2 LPARs) and strong hands-on delivery in AWS/Kubernetes. Built secure Jenkins-to-EKS pipelines with SonarQube/Trivy gates and resolved a widespread CVE-driven build outage by patching the Debian base layer. Also created reusable Terraform modules with remote state/locking and automated drift detection to provision full mirror environments in under an hour.”

Rupak Chand

Screened

Junior ML Data Associate specializing in AI training data and LLM prompt evaluation

Connecticut2y exp

“Applied ML/embodied AI practitioner who built an on-device gesture-control system for smart-home lights using Raspberry Pi + camera, focusing on privacy-preserving real-time inference and hardware-constrained optimization (async pipeline + TF Lite INT8). Also made a high-impact architecture decision for an ML content evaluation/QA pipeline processing millions of annotated text samples weekly, reducing batch runtime from ~6 hours to ~40 minutes while lowering compute cost.”

Nandini Reinthala

Screened

Mid-Level Full-Stack Python Developer specializing in AI and data platforms

Dallas, TX5y exp

“Full-stack engineer who builds TypeScript/React SPAs on Python (Flask/FastAPI) backends and has hands-on experience integrating AI components (Azure OpenAI, LangChain, vector databases) into user workflows. Has built internal AI-enabled dashboards/search tools for analysts and business users, emphasizing typed API contracts, CI/CD-driven quality, and microservices reliability patterns (monitoring, retries, idempotency) at scale.”

“Full-stack engineer with hands-on production experience building real-time customer-facing features (order tracking + push notifications) across React/React Native and Node/Spring Boot with Postgres/MySQL. Demonstrates strong reliability patterns (transactional outbox, background workers, idempotent webhook ingestion) and has deployed/operated systems on AWS (ECS/Fargate/ALB, CloudWatch, CodePipeline) with structured observability and environment separation.”

“Full-stack engineer with hands-on ownership of real-time, Kafka-driven systems in production, spanning React/TypeScript frontends, Spring Boot/Node backends, and AWS (EKS/ECS/EC2) operations. Notable for modernizing legacy batch workflows into event-driven architectures with measurable impact (35% faster risk calculations, 30% better accuracy) and scaling to 2x volume using reliability patterns like idempotency, retries, and staged rollouts.”

Ramcharan SreenivasaReddy

Screened

Mid-level Full-Stack Developer specializing in FinTech platforms and cloud-native microservices

Texas, USA6y exp

“Backend/platform-focused Python engineer who has owned FastAPI services with Postgres/SQLAlchemy and production-grade auth (JWT + RBAC). Experienced deploying and operating microservices on Kubernetes with GitOps (ArgoCD), HPA tuning, and Prometheus/Grafana monitoring, plus hands-on cloud-to-on-prem migrations and Kafka-based real-time streaming pipelines.”

Ganesh Pittala

Screened

Senior Software Engineer specializing in distributed systems and cloud-native platforms

New York, USA5y exp

“Backend-leaning full-stack engineer with experience at Walmart, Qualtrics, and American Express, shipping secure partner-facing API platforms and internal monitoring dashboards. Strong in AWS production operations (ECS/Fargate, RDS/Postgres, CloudWatch) plus rigorous testing/security practices, with measurable delivery and performance improvements (35% faster releases; ~30–40% latency reductions).”