Vetted Load Balancing Professionals

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“Backend engineer who has delivered large, measurable performance wins (10x throughput, 67% latency reduction) by combining Flask microservices, Redis caching, and AWS autoscaling/observability. Has hands-on depth in SQLAlchemy/Postgres optimization and production scaling pitfalls (cache consistency, connection exhaustion), plus experience deploying real-time ML inference (XGBoost) on AWS Lambda and building secure multi-tenant Kubernetes isolation.”

“Built and deployed a production compliance automation RAG system (at Citi) that generates citation-backed, schema-validated risk summaries for regulatory document review. Emphasizes regulated-environment reliability with retrieval-only grounding, abstention, confidence thresholds, and immutable audit logging, plus orchestration using LangChain/LangGraph and Airflow. Reported ~60% reduction in compliance review effort while maintaining high precision and traceability.”

Harshini Jaligama

Screened

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

Remote, USA5y exp

“Full-stack engineer (5+ years with Java/Spring Boot and React) who has built and deployed AWS-based microservices platforms using Kafka for real-time rewards/promotions and large-scale telemetry analytics. Demonstrates hands-on scalability expertise (partitioning, consumer groups, durability/acks, idempotency) and production-minded delivery practices (CI/CD, Docker, testing, Swagger, monitoring).”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

Bryan West

Screened

Senior Software Engineer specializing in AI, cloud infrastructure, and full-stack development

Chantilly, VA17y exp

“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”

Jeetendra Girish Vasisht

Screened

Junior Software Engineer specializing in cloud-native microservices

Bangalore, India2y exp

“Backend engineer (Nokia) who designs and migrates cloud-native microservices at scale, including a secure low-latency system handling 500k+ daily transactions. Strong in Kubernetes/OpenShift operations, CI/CD standardization, and production security (OAuth2/JWT/RBAC) with SOC2-aligned controls and zero critical security incidents. Demonstrated expertise in safe migrations (canary/blue-green, dual writes, reconciliation) and concurrency correctness in real-time systems.”

“SRE-oriented infrastructure engineer who built an internal Vertex AI/Gemini knowledge chatbot to centralize product and development documentation, cutting routine support questions from 10+ daily to roughly 2. Also brings hands-on experience debugging Kubernetes production incidents and monitoring ETL/data-quality issues in Dataflow-based pipelines.”

“Data engineer/backend engineer with healthcare domain experience at Centene, where they owned an end-to-end claims processing pipeline handling over 1 million monthly records. They combine Python/SQL pipeline work with API and event-driven service development, and cite a measurable 35% reduction in incident detection time through automated monitoring and validation.”

Naveena Musku

Screened

Mid-level AI/ML Engineer specializing in agentic AI and LLM systems

5y exp

“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”

“Built and iterated an end-to-end virtual waiting room for a real-time ticketing prototype, making concrete architecture tradeoffs (polling + Redis Pub/Sub) and improving performance post-launch with Redis caching (+30% throughput, -15% p99 latency). Also has hands-on experience building Spark/HDFS ETL pipelines with strong reliability/observability patterns and running disciplined NLP model evaluation loops on review-rating classification.”

“Database/platform engineer with hands-on ownership of large-scale GCP data systems in financial services, including customer-facing SaaS investment products with strict SLAs. Stands out for leading an on-prem-to-GCP modernization using Spanner, AlloyDB, Bigtable, and BigQuery, and for building Terraform/Python automation that cut provisioning time by ~70% while improving reliability and self-service.”

“Full-stack/product-minded engineer with strong distributed systems depth, spanning Spring Boot/Kafka microservices, Kubernetes observability, and large-scale React/TypeScript frontends. Particularly compelling for teams building real-time operational products: they describe owning payment/inventory services, designing telemetry dashboards for 150+ services, and helping move claims tracking from polling to event-driven architecture.”

“Engineer with experience in small, high-pressure innovation environments and enterprise healthcare platforms, spanning distributed systems, search, and database optimization. At RJ Lee Group, he helped pivot an Air Force document-processing platform from Pig/MapReduce to Apache Storm, enabling near-real-time results, and also built a full-stack natural-language search application that cut analyst investigations from months to weeks or days.”

“Backend engineer who built an NL-to-SQL system at Target, using a multi-step LLM pipeline with vector-store schema retrieval and SQL validation to safely answer business questions. Strong in production FastAPI systems (async, Pydantic, Docker/Uvicorn, load balancing) and security (OAuth2/JWT, scopes, and database row-level security), with experience migrating Flask apps to FastAPI + PostgreSQL using strangler/feature-flagged canary rollouts.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“Embodied AI / robotics-focused ML engineer with experience at JPMorgan and EY building language-to-robot control systems that connect transformer/LLM intent to safe real-world robotic actions. Designed production-grade, low-latency architectures (Kafka/Redis, monitoring, CI/CD) and applied sim-to-real and model distillation to make research ideas deployable on physical systems.”

“Backend/AI workflow engineer with experience at AirKitchenz, Uber, and Vivma Software, building production systems on AWS (Lambda, DynamoDB, Step Functions). Has a track record of major performance wins (DynamoDB latency 2s to <150ms; Postgres query 2s to ~180ms) and shipping LLM-powered onboarding and ticket-routing workflows with strong guardrails (schema validation, confidence thresholds, human-in-the-loop escalation).”

Rushir Bhavsar

Screened

Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training

1y exp

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

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.”

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.”

Mukundan Sridharan

Screened

Executive Technology Leader (CTO) specializing in IoT sensing, AI/ML, and RF/embedded systems

Rockville, MD22y exp

“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”

Jeevana Chilakapati

Screened

Mid-level Electrical Design Engineer specializing in power distribution and utility infrastructure

New Paltz, NY4y exp

“Electrical design engineer with hands-on ownership of residential building and telecom infrastructure projects, covering concept design, calculations, code compliance, and construction documentation. Particularly notable for balancing reliability, cost, and constructability in high-uptime telecom power systems while also bringing practical lighting design and multidisciplinary coordination experience.”

“Full-stack product engineer with hands-on experience building a React/serverless/SQL e-commerce platform for Haagen-Dazs and improving consumer UX in a location-based animal discovery app. Stands out for pairing strong technical fundamentals—component architecture, SQL performance tuning, reusable primitives—with measurable product outcomes like 40% more completed orders, 25% customer growth, 95% navigation accuracy, and 20% fewer device malfunctions.”