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Vetted Workflow Orchestration Professionals
Pre-screened and vetted.
Ram Kottala
Mid-level Data & GenAI Engineer specializing in lakehouse, streaming, and RAG platforms
“Built a production internal LLM-powered knowledge assistant using a RAG architecture (Python, LLM APIs, cloud services) that answers employee questions with sourced, grounded responses from internal documents. Demonstrates strong practical depth in retrieval tuning (chunking/metadata filters), orchestration with LangChain, and production reliability practices (latency optimization, automated embedding refresh, evaluation metrics, logging/monitoring) while partnering closely with non-technical operations teams.”
Naga Yanala
Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms
“Data engineer with healthcare and enterprise experience (Molina Healthcare, Dell Technologies) building and operating high-volume batch + streaming pipelines across AWS and Azure. Strong focus on data quality (schema validation, fail-fast checks), reliability (monitoring/alerts, retries), and performance tuning (Spark/partitioning), with measurable runtime reduction and improved downstream trust.”
“GenAI/ML engineer from Deloitte who built and shipped a production RAG-based internal search assistant for support teams, delivering quantified operational gains (20% effort reduction, 35% faster manual lookup). Experienced in enterprise-grade LLM reliability (grounding/hallucination control), compliance/security constraints, and rapid release cycles using CI/CD, MLflow, and orchestration tools (Airflow, Databricks Jobs, LangChain).”
Saaketh Sodanapalli
Intern AI/ML Engineer specializing in full-stack and data systems
“Built an LLM-powered customer segmentation agent during a Chewy internship, consolidating Snowflake data into a knowledge graph so non-technical marketing users could query customer cohorts in natural language. Stands out for combining agent/tooling design with rigorous data engineering practices, including schema audits, imputation, validation layers, and idempotent pipelines on messy large-scale datasets.”
Rekha Talla
Mid-level Full-Stack Software Engineer specializing in AI and document automation
“Backend/AI infrastructure engineer focused on production-ready LLM systems and distributed workflows. They described building a RAG-based multi-step agent with strong reliability controls, evaluation loops, and graceful degradation that improved latency by 30%, retrieval accuracy by 15%, and reduced support workload by 40%.”
“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.”
Pradeep Konda
Mid-level Software Engineer specializing in AI agents and full-stack cloud platforms
“Full-stack engineer who owned an enterprise AI agent automation platform at KeyBank, building React/TypeScript interfaces and Python/AWS microservices for document-heavy business workflows. Stands out for translating complex AI automation into usable products for non-technical users, with reported productivity gains of about 35% and reduced manual processing.”
Nicholas Leytem
Staff Software Engineer specializing in high-throughput retail data systems
“Software engineer at a global public growth company since 2015, with experience owning a legacy Java ETL analytics application and helping design and launch a Kotlin/Java data sourcing platform for customer order data. Brings hands-on experience across React, Spring Boot, AWS/Kubernetes deployment, and multi-database architectures, including an inventive pseudo-graph implementation in DynamoDB to support flexible document matching.”
Patrick Klima
Director-level Product Leader specializing in AI-powered B2B SaaS and identity platforms
“Product leader with a rare mix of zero-to-one AI/ML platform building, enterprise SaaS UX simplification, and people leadership across product and design. They’ve launched identity verification and workflow products at scale, delivered measurable commercial impact, and bring an authentic EdTech background shaped by direct student interviews and teaching experience.”
Sai Garipally
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and computer vision
“Built and productionized a multi-agent, LLM-powered document understanding system to replace manual review of long documents, using LangGraph orchestration plus RAG to reduce hallucinations. Implemented layered reliability controls (structured templates, checker agent, and human-in-the-loop feedback) and reported ~40% speed improvement after orchestration; also has hands-on Airflow experience for scheduled data pipelines.”
Abdul Mohammed
Mid-level Data Analyst specializing in healthcare and financial analytics
“Built and productionized an LLM-powered clinical documentation and insights pipeline at Cardinal Health using LangChain + GPT-4 with RAG to summarize long clinical notes, extract medication/dosage entities, and generate structured SQL-ready outputs for downstream analytics. Emphasizes clinical reliability via labeled benchmarking (precision/recall/F1), shadow deployments, clinician human-in-the-loop review, and ongoing monitoring/orchestration with Airflow, Lambda, S3, Postgres, and Power BI.”
Ramtin Kazemi
Junior Full-Stack Software Engineer specializing in Django, AWS, and AI/ML
“Full-stack engineer who built and owned an AI-powered personal statement editor in Next.js (App Router + TypeScript), including dynamic routing, server-side data fetching, and typed API route handlers. Post-launch, they handled production monitoring/debugging and shipped reliability/performance upgrades (rate limiting, retries, rollback, DB indexing), and report a 40% latency reduction using Suspense/streaming and React concurrency patterns. Also implemented a durable Temporal-orchestrated AI document workflow with robust retry/idempotency strategies.”
“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
Mid-level Software Engineer specializing in distributed systems and full-stack platforms
“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.”
Nithin Raghava Aitha
Mid Software Engineer specializing in FinTech and ML-powered backend systems
“Backend-leaning full-stack engineer who has shipped real-time, customer-facing dashboards and ticketing/payment features at Freshworks and Global Payments. Strong in Python API design (Django/Flask/FastAPI) and React/TypeScript UIs, with hands-on experience scaling PostgreSQL for high transaction volumes and operating services on AWS, including incident response and HIPAA-aligned security controls.”
Aravind Konakondla
Mid-level Full-Stack Software Engineer specializing in AI automation and FinTech
“Built and owned an AI-powered document automation platform for enterprise customers, covering frontend, backend APIs, workflow orchestration, and downstream integrations. Stands out for translating AI extraction into a simple, trust-building product experience for non-technical users, with strong attention to production reliability, human review flows, and measurable quality metrics.”
Keerthana Priya
Mid-level Data Analytics & ML Engineer specializing in NLP, LLMs, and cloud data platforms
“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”
Sravan Kumar Jajam
Mid-level Data Scientist / ML Engineer specializing in streaming ML systems for healthcare and IoT
“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”
Rasheed Mohammed
Senior Site Reliability Engineer specializing in multi-cloud Kubernetes and DevSecOps
“Cloud/Kubernetes-focused production engineer with experience running 99.95% uptime platforms across AWS/Azure/GCP. Strong in incident response and performance troubleshooting (including a 30% MTTR reduction), and in building secure CI/CD and Terraform-based IaC for AKS/GKE microservices with robust change controls and rollback practices. Notably does not have direct IBM Power/AIX/VIOS/HMC or PowerHA/HACMP ownership.”
Rishitha reddy katamareddy
Mid-level Generative AI & Machine Learning Engineer specializing in agentic LLM systems
“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”
Melanie Gittiban
Executive Healthcare Operations Leader specializing in AI-enabled value-based care
“Operations leader with deep healthcare delivery experience who joined a struggling VC-backed company and helped architect a full strategic pivot away from a fragmented home care LTSS agency model into a scalable chronic care telehealth/value-based care platform. Designed the end-to-end virtual care operating model (clinical team structure, triage/escalation workflows, EHR/RCM/call center ops) and rebuilt performance management around leading indicators to help leadership steer scaling decisions.”
Sreelekha Vuppala
Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms
“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”
Naga Renuka Kandi
Junior Software Engineer specializing in cloud, full-stack development, and Generative AI
“Built and shipped a production Chrome extension (Promptly) that lets users select text on any webpage and transform it in place (rewrite/shorten/translate) using on-device AI plus external LLMs. Implemented a custom lightweight orchestration layer for prompt chaining, context flow, and output validation, and tackled tricky browser Selection API issues to preserve formatting while keeping the UX simple and fast.”