Vetted Apache Kafka Professionals

“Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.”

“Built and shipped production systems spanning real-time operational dashboards and an LLM-powered internal documentation assistant using RAG (embeddings + vector DB). Demonstrates strong focus on reliability and iteration: implemented guardrails and evaluation loops (human review, hallucination tracking, regression prevention) and improved performance/scalability through query optimization, caching, and retrieval tuning.”

“Backend/data engineer focused on AWS-based, event-driven systems—building Golang microservices and serverless pipelines with strong data validation, observability (CloudWatch/Splunk/New Relic), and reliability patterns (retries/DLQs). Has also operated distributed web scraping/data collection with schema versioning and Step Functions backfills, and ships well-documented, versioned REST/WebSocket APIs for internal and external consumers.”

“Data engineer with experience at Wells Fargo and Accenture owning end-to-end production pipelines processing hundreds of millions of transactional/risk records daily. Strong focus on data quality and reliability (reconciliation checks, schema drift detection, CloudWatch alerting) plus Spark performance tuning and idempotent backfills using Delta Lake/merge logic across AWS (S3/EMR/Databricks/Redshift) and Azure (ADF/Azure DevOps/Azure Monitor).”

“Data engineer with experience across healthcare and geospatial risk systems, owning end-to-end pipelines from ingestion through serving on AWS/Azure stacks. Built HIPAA-compliant data quality gates and CDC for millions of daily claims, and also delivered a real-time wildfire risk platform with 20-minute refresh cycles and a 60% data accuracy lift. Strong in streaming (Kafka), Spark performance tuning, and production-grade orchestration/CI/CD (Airflow, Docker, Jenkins, GitHub Actions, Terraform).”

“Data engineer/backend engineer with experience in healthcare (Cardinal Health provider enrollment) and finance (Northern Trust) building and stabilizing data pipelines and REST services. Worked with APIs and Kafka at ~200k–300k records/day, improving data quality (DLQ + validation), performance (SQL/indexing), and reliability/observability (logging, alerts, consumer lag metrics), and stood up an early-stage financial data service with Jenkins-based CI/CD.”

“Data engineer at CenterPoint Energy who built and operated multiple production-grade GCP data systems: a daily Snowflake→BigQuery replication framework (150+ tables) with Monte Carlo/Atlan-driven observability and schema-drift protection, plus a FastAPI metrics service for pipeline health. Demonstrated measurable impact (40% faster dashboard queries, 70% less manual refresh work, zero data loss) and strong operational rigor (scaling Cloud Run jobs, SAP SLT reconciliation, quarantine patterns, CI/CD via GitHub Actions + Terraform).”

Sri Lalitha

Screened

Senior Full-Stack Java Engineer specializing in cloud-native microservices and FinTech

California, USA6y exp

“Backend engineer who owned a Python task management API with JWT auth, async notifications, and performance work (DB optimization/caching) to handle high volumes. Led an on-prem to Azure private cloud migration at Morgan Stanley using GitOps and IaC (Terraform/ARM) with phased rollout and rollback planning. Also built a Kafka real-time streaming pipeline with exactly-once/idempotent consumers and Prometheus/Grafana monitoring.”

Yijun Chen

Screened

Senior Full-Stack Software Developer specializing in IoT and cloud systems

Toronto, ON4y exp

“Frontend-focused engineer who built a full movie recommendation system from concept to production, comparing classic collaborative filtering with LLM-based recommendation approaches on AWS. Emphasizes scalable architecture, strict TypeScript data contracts, and high-quality Next.js/React UI patterns (defensive states, scoped state management, performance optimization) with disciplined QA and feature-flagged rollouts.”

Manasa Reddy Nagendla

Screened

Mid-level Full-Stack Java Engineer specializing in microservices, cloud, and event-driven systems

Cincinnati, OH6y exp

“Software engineer at Procter & Gamble focused on warehouse/operations systems, building near-real-time order/inventory visibility using Java/Spring Boot, React, Kafka, PostgreSQL, and Redis with measurable latency and load-time gains. Also shipped internal LLM/RAG knowledge assistants grounded in company runbooks and workflows, implementing guardrails and an evaluation loop that drove concrete retrieval improvements (document chunking) and regression prevention.”

Sana Khan

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time inference in FinTech

Oklahoma, USA4y exp

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

Kaushik Balakesavalu

Screened

Mid-level Full-Stack Java Developer specializing in enterprise SaaS and FinTech

Fairfax, VA5y exp

“Software engineer with fintech/retirement-fund domain experience who led an internal dashboard consolidating fund transactions, approvals, and reporting into a single workflow tool. Strong in full-stack delivery (React + REST APIs + DB optimization) and in scaling/cleaning messy operational data via modular ETL pipelines (Python/Node), iterating post-launch with performance improvements like caching, pagination, and enhanced filtering.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

Narendra R

Screened

Senior Full-Stack Java Developer specializing in microservices and cloud platforms

Dallas, TX7y exp

“Backend engineer focused on scalable Python/Flask services and high-performance PostgreSQL/SQLAlchemy systems, with demonstrated wins like reducing N+1-driven response times to under 200ms and cutting P95 latency below 1s via background queues and caching. Has production experience operationalizing ML models as Dockerized APIs on AWS (S3/Lambda) with monitoring (CloudWatch/ELK), plus robust multi-tenant isolation using JWT-driven tenant context and row-level security.”

Meghanath kethireddy

Screened

Mid-level Full-Stack/Backend Engineer specializing in Java microservices and cloud platforms

Dallas, TX5y exp

“PayPal ML/AI practitioner who built and productionized a hybrid recommendation engine (BERT/LLM embeddings + collaborative filtering + XGBoost ranking) on AWS with end-to-end MLOps and orchestration. Addressed real-world issues like cold start and embedding latency (ONNX, clustering, caching, PySpark/Delta Lake) and drove a 27% lift in upsell conversion via A/B testing and stakeholder collaboration with marketing.”

Ashwitha Reddy

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices and AWS

Ohio, United States3y exp

“Backend/platform engineer who has owned a real-time business analytics dashboard backend (Python/Flask/MongoDB) and built Kafka event-streaming pipelines with idempotent processing and DLQs. Strong DevOps/GitOps experience deploying containerized microservices to AWS EKS with CI/CD (Jenkins/GitHub Actions/CodePipeline) and ArgoCD auto-sync/drift detection, plus hands-on support for phased hybrid cloud/on-prem migrations using feature flags and replication.”

Mukul Sai Pendem

Screened

Mid-level Full-Stack Engineer specializing in cloud-native microservices and DevOps

United States (Remote)4y exp

“Backend engineer with strong Python/FastAPI microservices ownership, including an ML-serving service with embeddings, async DB access, and Redis caching to reduce latency under high load. Experienced deploying and operating containerized services on Kubernetes using GitOps (Argo CD/Helm) with automated CI/CD, plus hands-on Kafka streaming pipeline tuning and enterprise migration work (Infosys) using blue-green/active-passive strategies.”

“Has worked across multiple startup stages from pre-funding through Series D and emphasizes rigorous idea validation through direct conversations with both end users and purchasing decision-makers. Interested in applying NLP to automate summarization/abstracting of highly technical articles, with a balanced view of entrepreneurship that prioritizes health and family.”

Chakravarthy V P

Screened

Executive AI Consultant/CTO specializing in Agentic AI, GenAI, and cloud-native data platforms

Texas, USA21y exp

“Bootstrapped founder and CTO of C4Scale, a 2.5-year-old services-led company delivering MVP-to-scale product/platform builds for high-value clients across 5+ countries (10+ projects). Strong fit for roles blending scalable SaaS platform engineering, technical org leadership, and practical AI adoption, with clear awareness of the operational and GTM challenges of scaling into enterprise.”

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

Rohit Vibhu Channananjundarya

Screened

Mid-level Software Engineer specializing in distributed systems and full-stack platforms

Chicago, IL6y exp

“Engineer who treats AI as a force multiplier rather than a replacement for judgment, with hands-on experience using tools like Claude Code, Cursor, Copilot, and Codex across planning, coding, testing, and review. Particularly notable for building a multi-agent PR review system that automated summarization, risk scanning, schema validation, and test suggestions, helping the team shift reviewer time toward architecture and business logic.”

“Built an internal RAG assistant for financial documents using FastAPI, OpenAI APIs, and vector search, improving document search speed and reducing manual effort for the business team. Stands out for a pragmatic approach to AI engineering: uses AI heavily for productivity, but keeps human judgment central and has designed retrieval, validation, and summarization workflows end-to-end.”