Vetted Kubernetes 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.”

Bandla Sai Giridhar

Screened

Mid-level Software Engineer specializing in full-stack and cloud-native microservices

Dallas, TX4y exp

“Backend engineer who built a Python/Flask system for high-volume healthcare claims processing, using PostgreSQL as the source of truth and RabbitMQ workers for scalable async processing. Experienced in SQLAlchemy/Postgres performance tuning, multi-tenant data isolation (including Postgres RLS), and integrating/versioning ML model services (scikit-learn/PyTorch/Hugging Face) with controlled rollouts. Drove measurable performance gains by batching background jobs and adding Redis caching (40% less workload; response times cut from ~10s to 2–3s).”

Sai Swetha Bodlapati

Screened

Senior Data Engineer specializing in Spark, Kafka, and Databricks Lakehouse platforms

Dallas, TX5y exp

“Data engineer at Fidelity who built and operated a real-time financial transactions lakehouse on AWS/Databricks, processing millions of records daily with Kafka streaming. Demonstrated strong reliability and data quality practices (watermarking, idempotent Delta writes, validation/reconciliation, observability) and delivered measurable improvements (~30% faster jobs and ~30% fewer data issues) while enabling trusted gold-layer analytics for downstream teams.”

“Analytics-focused candidate with experience building SQL/Python pipelines and dashboards for donor, campaign, and website performance reporting. They have worked with messy multi-source data, standardized metric definitions, and delivered automated reporting that reportedly reduced manual effort by about 80%.”

“ML/AI engineer with hands-on ownership of production recommendation and RAG systems at Northern Trust. They combine transformer modeling, latency optimization, cloud deployment, and monitoring with measurable business impact, including 14% accuracy gains, 12% engagement improvement, and 19% better query relevance.”

“Healthcare ML/AI engineer at Cigna who has owned a clinical RAG pipeline from prototype through production, monitoring, compliance, and iteration. Stands out for combining LLM product delivery with healthcare-grade safety and explainability, driving a 38% retrieval precision gain, 42% hallucination reduction, and meaningful improvements in team velocity and system reliability.”

Sree Damineni

Screened

Mid AI/ML Engineer specializing in financial and insurance analytics

USA3y exp

“Senior AI/ML engineer focused on production ML, LLMs, and MLOps, with concrete experience shipping fraud detection and enterprise RAG systems. They combine strong deployment and monitoring discipline with measurable business impact, including 31% precision improvement in fraud detection and 37% better answer relevance in a financial-document QA system.”

“Built end-to-end ML and GenAI systems at Northern Trust, including a production RAG-based document intelligence platform for financial reports and contracts. Stands out for combining strong MLOps execution with practical product judgment—improving forecast accuracy by 22%, document review accuracy by 38%, and cutting deployment time by 45% while keeping latency and reliability production-ready.”

“ML/AI engineer with strong production ownership across predictive ML and Generative AI systems. They’ve delivered measurable business impact through real-time churn/drop-off prediction, RAG-based document QA, and scalable LLM optimization, with a consistent focus on reliability, safety, latency, and developer productivity.”

Upal Kundu

Screened

Junior Security Engineer specializing in cloud security and DevSecOps

Fort Worth, TX3y exp

“Candidate has hands-on experience building and debugging cloud-based backend workflows across AWS and GCP, including a remote desktop deployment for HP, email-to-Google-Sheets automation, and AI/voice backend testing. They stand out for practical infrastructure troubleshooting, API integration work, and lightweight LLM application development with attention to latency, cost, and operational stability.”

“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.”

Niranjaan Munuswamy

Screened

Mid-level Full-Stack Software Engineer specializing in cloud and data engineering

Chicago, IL4y exp

“Backend engineer with experience at Cigna evolving REST API services backed by PostgreSQL, emphasizing reliability/correctness, scalability, and observability. Has hands-on production experience with FastAPI (contract-first design, Pydantic schemas), performance tuning (indexes, caching), and secure auth patterns (OAuth/JWT, RBAC, row-level security via Supabase), plus low-risk incremental rollouts using feature flags and dual writes.”

William Goff

Screened

Principal Software Engineer specializing in MongoDB and Java platforms

San Mateo, CA10y exp

“Government and defense software architect with a track record of building mission-critical systems from prototype to long-lived production. Most notably designed Banyan at Fort Meade, a federated SOA platform that achieved 99.9999 uptime and handled 3.2 million daily queries, and later built SBOM automation at Lockheed Martin that was contributed back to the CycloneDX open source ecosystem.”

“Built production AI systems for Macy's and Bloomingdale's, including an embeddings-based pipeline to clean trending search queries and an end-to-end 'Ask Macy's' multi-agent chat experience. Brings hands-on experience with real-world agent orchestration, tool integration, quality evaluation, and business-facing safeguards in a large-scale e-commerce environment.”

“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.”

“Software engineer who recently shipped an authentication and role-based access feature for a web app, using AI selectively for boilerplate, debugging, and test suggestions while retaining ownership of architecture, security, testing, and final review. Stands out for a disciplined, security-first approach to AI-assisted development and a pragmatic preference for lightweight API-driven solutions over unnecessary framework complexity.”

“Backend engineer who shipped a Spring Boot transactions/points feature while using AI selectively for boilerplate and test ideas, keeping core system design decisions in human hands. Stands out for rigorous validation of AI output against database behavior, concurrency, idempotency, and failure scenarios, and has also built a lightweight planner -> code gen -> validator agent-style pipeline with step-level logging and retries.”

“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 software engineer focused on multi-robot fleet orchestration in ROS 2, owning the fleet manager and task dispatch layer for pick/drop workflows. Strong in real-world reliability and safety (heartbeats, idempotent tasking, E-stop/localization confidence gates) and in debugging timing/state issues via telemetry alignment and rosbag replay, with experience in simulation, CI/CD, Docker, and Kubernetes-based deployments.”

“Paystand engineer/intern who built a multi-agent LLM orchestration system (with logging/feedback loops) that became part of the team workflow and reportedly cut development time ~70%. Partnered with sales/product on enterprise demos and implemented a dynamic RBAC system that helped drive adoption of an intern-built product to multiple enterprise clients, contributing to seven-figure ARR. Also founded and pitched a student-entrepreneur business management/payments project (HustleHub) and won a university startup competition.”

“Product-minded full-stack engineer at a B2B startup who ships high-stakes customer-facing features fast: delivered a Spanish AI support agent in 2 weeks by benchmarking LLMs and using native Spanish system prompts, reaching 90% resolution. Built the company’s first monetization system (hybrid subscription + usage) with Stripe/Firebase, emphasizing secure JWT-based flows and idempotent webhooks, and led a microservices decoupling effort that cut developer onboarding time by 50%.”