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Vetted Prompt Engineering Professionals
Pre-screened and vetted.
Sai Charan Kolla
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps on AWS
“LLM engineer who built a production document intelligence/RAG pipeline to extract structured data from thousands of unstructured PDFs, cutting manual review time by 60%. Experienced with LangChain and Airflow orchestration plus rigorous evaluation (labeled datasets, prompt testing, HITL review, monitoring) to improve accuracy and reduce hallucinations while partnering closely with non-technical operations stakeholders.”
Hari Chandana Kasula
Entry Machine Learning Engineer specializing in NLP, computer vision, and recommender systems
“Built and shipped an end-to-end podcast recommendation system exposed via a Flask API and React UI, explicitly balancing relevance, diversity (MMR), and safety constraints while meeting ~200ms latency targets. Also implemented a production-style RAG/information-extraction pipeline using web retrieval, spaCy NER, and fine-tuned SpanBERT with guardrails and evaluation loops (precision/recall/F1) to tune confidence thresholds and improve reliability.”
“LLM engineer who has deployed production RAG systems for regulated document QA (PDFs/knowledge bases), emphasizing grounded answers with citations, RBAC, monitoring, and continuous feedback. Demonstrates deep practical expertise in retrieval quality (semantic chunking, hybrid BM25+embeddings, re-ranking), reliability (guardrails, deterministic workflows), and measurable evaluation (golden sets, log replay, A/B tests) while partnering closely with compliance/operations stakeholders.”
Dhyey Desai
Intern AI/ML Engineer specializing in RAG, multimodal AI, and LLM systems
“Built and shipped 'PetPulse,' a production AI pet-health note system that records voice notes, transcribes them, converts transcripts into structured symptom/event data, and supports grounded Q&A over a user’s notes and vet PDFs. Demonstrates full-stack LLM product execution (FastAPI + GPT-4 + Firebase), with concrete reliability/performance work (async endpoints, caching, RAG/embeddings, function calling) and user-centered iteration with a non-technical product stakeholder.”
Divyam Agrawal
Mid-level Machine Learning Engineer specializing in LLMs and NLP classification systems
“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”
Adithya Chittajallu
Mid-level AI/ML Engineer specializing in LLM systems, MLOps, and Healthcare AI
“Built and shipped a production-grade agentic RAG system at CVS Health for patient adherence and medication recommendations, processing 20k+ patient records/day. Strong focus on real-world reliability: hybrid retrieval tuned with re-ranking (<400ms latency), strict JSON/schema validation and tool guardrails, and monitoring/drift detection that reduced MTTD from 6 days to 18 hours while improving recommendation accuracy (+8%) and cutting escalations (~23%).”
“Full-stack product engineer with hands-on ownership across React/TypeScript, serverless backends, and Postgres, combining technical depth with strong UX instincts. Stands out for measurable impact: improved a slow query from seconds to under 200ms and increased onboarding completion by about 25%, while also building reusable platform primitives and scalable multi-tenant configuration systems.”
Uppalapati Harika
Senior Salesforce Developer specializing in Financial Services and Healthcare
“Salesforce-focused candidate with hands-on experience owning Sales Cloud automation end to end, including requirements translation, Flow/Apex design, testing, and production deployment. They show practical judgment in choosing hybrid declarative/code solutions, bulk-safe Apex patterns, and modern UI work across both LWC and legacy Aura components.”
Anirban Ghosh
Mid-level Machine Learning Engineer specializing in data science and cloud systems
“ML engineer who independently pitched and built a recommendation engine at Danske Bank in a legacy fintech environment, creating compliant data pipelines and deployment infrastructure from scratch and delivering a 62% engagement lift with 70%+ advisor adoption. Also worked at AWS on classification and GenAI-powered reporting systems, with strengths spanning production ML, platform setup, monitoring, and research-to-production optimization.”
Chaitanya Prasad Reddy Narala
Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems
“Senior AI/ML engineer focused on production LLM systems, combining RAG, fine-tuning, distributed training, and AI safety to ship scalable real-time moderation and conversational AI platforms. Stands out for pairing deep AWS/Kubernetes MLOps expertise with measurable impact: 40% lower latency/cost, 30-50% fewer hallucinations, and major reliability gains through observability and automation.”
Balaji Nissenkarao
Mid-level Machine Learning Engineer specializing in AI/LLM systems
“ML/LLM systems engineer who has owned AI support automation products end-to-end, including ServiceNow-integrated incident routing, RAG-based resolution suggestion systems, and production stabilization. Stands out for combining hands-on platform work across PySpark, AWS Glue, FastAPI, Kubernetes, and Pinecone with measurable operational impact, including 30-35% MTTR reduction and 25-30% improvement in first-touch resolution.”
Manish Challa
Mid-level AI/ML Engineer specializing in Generative AI and financial services
“ML/AI engineer with hands-on experience shipping regulated financial AI systems at JPMC and Capgemini, spanning credit risk, fraud detection, and generative AI assistants. Stands out for combining modern LLM/RAG architectures with strong MLOps, real-time infrastructure, and explainability/compliance practices, while delivering measurable business impact in latency, accuracy, cost, and risk reduction.”
Sachin Komati
Mid-level AI/ML Engineer specializing in GenAI, RAG, and healthcare ML
“Built an end-to-end GenAI/RAG platform for financial compliance and research at BlackRock, focused on safe, auditable answers in a highly regulated environment. Combines strong LLM engineering depth with production platform skills and delivered clear business impact, including reducing research/compliance turnaround from hours to seconds, improving retrieval relevance by 22%, and cutting inference costs by 75%.”
Farhan Shahbaz
Senior Software Engineer specializing in cloud infrastructure and platform engineering
“Backend engineer with deep experience in security and access-management platforms at JPMorgan Chase, including owning automation for migrating 50+ engineering teams from CyberArk to HashiCorp Vault. Stands out for combining regulated-environment rigor, infrastructure automation, and production operations with practical AI integration in internal access workflows.”
Malyadri Peruru
Senior Software Engineer specializing in backend, cloud, and IoT automation
“Software engineer with strong systems design experience spanning both core product infrastructure and AI-enabled support workflows. Co-led a from-scratch B2B SaaS billing platform for complex customer pricing models, and also improved an orchestrated agent system by tightening context, metadata, and human-review safeguards for misrouted support tickets.”
Adithi Anugu
Mid-level Full-Stack Software Engineer specializing in API and data-driven applications
“Full-stack engineer with internship experience at Sports Excitement and AI systems work at the University of Florida, combining React/TypeScript, Node.js, and PostgreSQL with applied AI workflow tooling. Stands out for shipping measurable product improvements—30% faster pagination and 40% higher engagement—and for building observability, eval, and human-review layers around AI-assisted data extraction systems.”
Andy Trus
Senior Product Leader specializing in EdTech and AI-powered learning products
“EdTech product leader with deep experience across K-12 and higher education, spanning $100M-scale platform strategy, 0-to-1 learning products, and AI-enabled assessment workflows. Particularly compelling for roles at the intersection of AI and education: they built an LLM-based content QA tool that saved $2.75M and articulate a nuanced philosophy around using AI to amplify human tutoring rather than replace it.”
Anton Artemov
Senior AI Product Manager specializing in pricing, experimentation, and data products
“Product leader at VicRoads who rebuilt the Custom Plates digital portfolio, including an AI-powered pricing and recommendation platform that drove more than 20% revenue uplift. Stands out for combining commercial product thinking with explainable AI, regulatory compliance, and legacy-system modernization in a government-related environment.”
Anshika Bajpai
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
“Engineer with impactful experience at Palo Alto Networks and Optum, focused on production automation and AI-powered internal tools. Built and owned an end-to-end RAG knowledge system adopted by 1000+ internal users with roughly 75% faster response times, and also transformed a legacy Optum coverage-feed workflow from 500+ minutes to under 3 minutes through data standardization and microservices refactoring.”
“Forward-deployed, full-stack/platform engineer who owns production features end-to-end across frontend, backend, data, and infrastructure (AWS serverless, Terraform, React). Has modernized critical fintech/payment systems (zero-downtime monolith-to-microservices with Kafka event sourcing) and productionized AI-native support workflows (LLM + RAG on Pinecone) with measurable gains in latency, incidents, CSAT, and support efficiency.”
Jayanti Lahoti
Junior Software Engineer specializing in full-stack systems, ML, and robotics perception
“Robotics software engineer with autonomous driving lab experience at UCSD, building and optimizing ROS2 perception and control pipelines (camera-based real-time object detection) with a strong focus on low-latency performance and robust message interfaces. Also brings production deployment experience from Hewlett Packard Enterprise, using Docker and Kubernetes for containerized environments and deployment pipelines.”
Arman Riahi
Senior Software Engineer specializing in identity, integrations, and cloud platforms
“Customer-facing technical/product professional with hands-on experience delivering an LLM-driven document processing feature from design to production, including monitoring, logging, and LLM evals. Demonstrates a pragmatic approach to agentic/LLM workflows (using deterministic logic where possible), strong stakeholder alignment, and sales enablement through demos, tutorials, and direct customer calls; has presented to principal engineers (Intuit) and taught coding bootcamps (eBay).”
Darshi Shah
Junior Product/UX Designer specializing in AI-powered experiences and human-centered research
“Product/UX designer building an end-to-end startup MVP for small banks/credit unions to benchmark against regional competitors, including a custom design system and working prototypes created with Cursor/v0 and prompt engineering. Has experience translating complex finance analyst workflows into intuitive UX and running structured moderated usability tests with quantitative metrics; also led sensitive health research (20+ users) that shifted direction away from AI chatbot replacement.”
Dheeraj Vajjarapu
Mid-level AI/ML Engineer specializing in MLOps, NLP/LLMs, and computer vision
“Built and shipped a production LLM/RAG risk-case summarization and triage system used by fraud/compliance analysts, with strong grounding controls (evidence-cited outputs and refusal on low confidence). Demonstrates end-to-end ownership across retrieval quality, Airflow-orchestrated indexing pipelines, and compliance-grade privacy (PII redaction, RBAC, encrypted redacted logging, and auditable prompt/model versioning) plus a tight feedback loop with non-technical domain experts.”