Vetted scikit-learn Professionals

“Data engineer focused on AWS-based enterprise data platforms, owning end-to-end pipelines from multi-source batch/stream ingestion (Glue/Kinesis/StreamSets/Airflow) through PySpark transformations into curated datasets for Redshift/Snowflake. Emphasizes production reliability with strong monitoring/observability and data quality gates, and reports ~30% performance improvement plus improved SLAs and latency after optimization.”

“Data engineer with healthcare domain experience who has owned end-to-end pipelines and APIs at UnitedHealth Group, processing ~8M records per batch. Strong focus on data quality (multi-layer validation), reliability (monitoring/logging, retries/idempotency), and performance (Spark/SQL tuning, caching), with experience standing up early-stage systems using Python, Docker, and CI/CD.”

“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”

Chandan Chalumuri

Screened

Mid-level Data Scientist specializing in ML, NLP, and Generative AI

Tempe, AZ4y exp

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

Manpreet Kour

Screened

Senior Data Scientist specializing in Generative AI and NLP

Seattle, USA6y exp

“ML/NLP engineer with recent Scotiabank experience building production-grade indexing automation over large-scale emails and customer databases, combining LLM fine-tuning (Mistral, XLM-R) with fuzzy matching to exceed 95% accuracy under strict banking constraints. Also built a RAG-based chat agent using Gecko embeddings, Vertex AI Search, Gemini, and cross-encoder reranking, and delivered a text-to-SQL chatbot at SOTI through iterative fine-tuning and benchmark-driven experimentation.”

Sharanya Rao

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare

Remote, USA3y exp

“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”

Asvad Shaik

Screened

Mid-level AI/ML Engineer specializing in Generative AI, RAG, and NLP

Dallas, TX5y exp

“Backend engineer who built and migrated a large-scale document intelligence platform used by legal, healthcare, and insurance clients, processing millions of pages. Experienced moving from a monolithic, LLM-heavy approach to a modular FastAPI service architecture with ML classification + RAG, strong validation/auditability, and enterprise security (JWT/OAuth, RBAC, PostgreSQL RLS) with zero-downtime incremental rollouts.”

“Data Engineer (TCS) who owned an end-to-end CRM analytics pipeline for Bayer’s eSalesWeb integration, ingesting from Salesforce APIs/databases/S3 and serving analytics-ready datasets via PostgreSQL/S3 for Tableau. Drove measurable outcomes: ~60% reduction in manual data-quality effort, ~30% lower latency through SQL optimization, and ~35% improved stability via monitoring, retries, and idempotent processing.”

Teage Johnson

Screened

Junior Full-Stack Engineer specializing in FinTech and machine learning

California, USA3y exp

“Software engineer at early-stage startup Cari with hands-on experience shipping AI-enabled production workflows, including an LLM chatbot for a micro-transit platform and an automated image-processing pipeline integrated with Claude. Stands out for combining practical agent reliability patterns—schema validation, fallbacks, caching, and idempotency—with strong ML evaluation instincts and experience cleaning messy operational invoice data.”

varshini yaganti

Screened

Mid-level Data Analyst specializing in financial and customer analytics

Marietta, GA4y exp

“Analytics professional with experience at KPMG and Robosoft Technologies, working across financial and customer engagement data. They combine SQL, Python, experimentation, and BI dashboards to turn messy multi-source data into decision-ready insights, including a pricing test that improved conversion rates by 9%.”

“Full-stack product engineer who has owned complex internal platforms end-to-end, spanning React/TypeScript frontends, Flask/Redis backend systems, and relational data design. Particularly strong at turning technically dense workflows into intuitive user experiences, including a synthetic-imagery platform adopted by multiple Army research labs and a marketing analytics system with 99.99%+ uptime.”

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

Phani K

Screened

Mid-level AI/ML Engineer specializing in GenAI, NLP, and healthcare-financial ML

Terre Haute, IN4y exp

“ML/AI engineer with hands-on experience shipping healthcare AI systems, including an oncology risk prediction platform and RAG-based clinical decision support tools. Stands out for combining clinical domain context with strong production engineering across Spark, FastAPI, AWS SageMaker, monitoring, evaluation, and safety guardrails.”

Apoorv Bankey

Screened

Mid-level Backend Engineer specializing in distributed systems and FinTech

New York City, NY6y exp

“Engineer who uses AI and multi-agent workflows as a force multiplier while keeping architecture, security, scalability, and production quality under human control. Shared a concrete example of accelerating a backend-heavy SaaS email ingestion platform with authentication, role-based APIs, database models, and deployment setup using agent-style development and review.”

“ML engineer at CGI who built demand forecasting models end-to-end, from feature engineering and training through AWS deployment. Stands out for a production-first mindset and strong skepticism of AI-generated code, including catching a Copilot-generated SQL query that would have caused a costly full table scan in production.”

KUSHWANTH MUTHYALA

Screened

Senior Full-Stack Engineer specializing in FinTech

Irvine, CA6y exp

“Engineer with hands-on experience building and deploying end-to-end ML inference pipelines using SageMaker, TensorFlow, Scikit-learn, and Kafka-backed real-time data systems. Brings a strong distributed-systems mindset and has already operated in a tech lead capacity through architecture decisions, code reviews, and cross-functional coordination. Especially compelling for teams building production AI/ML platforms that need both practical execution and sound engineering judgment.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

Pallavi Hogalagere Anandareddy

Screened

Mid Software Engineer specializing in backend systems and FinTech

Tampa, FL4y exp

“Backend/full-stack engineer with experience spanning banking implementations at FIS and hands-on product building in React, TypeScript, Node.js, SQL, and Python. Stands out for owning six bank go-lives end-to-end, building an AI resume evaluator, and proactively automating manual operations workflows to reduce errors and improve consistency.”

“Backend-leaning full-stack engineer who has shipped both enterprise workflow software and AI-powered document intelligence products. Stands out for combining practical product judgment with strong production debugging skills across Spring Boot, GraphQL, FastAPI, vector search, and RAG systems, and for improving adoption by making AI search experiences intuitive for non-technical users.”

Raj Kalantri

Screened

Intern-level Software Engineer specializing in AI/ML and full-stack development

Kolkata, India1y exp

“Built a sophisticated AI career counselor as a full-stack web app for early-career students, integrating React, Flask, Pinecone, and LLM inference into a stateful conversational product. Stands out for combining hands-on debugging of retrieval/embedding pipelines with strong browser-performance instincts and pragmatic UX iteration based on real user testing.”

“Full-stack engineer who shipped a production "Financial Insight" assistant dashboard in Next.js App Router/TypeScript, integrating a RAG pipeline (embeddings + ChromaDB + LLM) via route handlers and owning post-launch performance (latency, token cost, retrieval relevance). Also built/optimized Postgres-backed workflows for an outbound dialer and callback routing engine handling ~10,000 daily contacts, validating query performance with EXPLAIN (ANALYZE, BUFFERS).”

“Full-stack engineer with hands-on experience building real-time analytics and logistics platforms across modern JavaScript and Java stacks. They combine strong production ownership and database optimization skills with architectural leadership, including redesigning bottlenecks with SQS/Lambda and driving a monolith-to-microservices migration on Kubernetes that cut deployment time by 50%.”

Sri Teja

Screened

Mid-level AI Engineer specializing in LLM systems and enterprise data platforms

Phoenix, AZ5y exp

“Built and owned key parts of Ripley, an AI-powered multi-agent operations platform for roadside assistance that automates high-volume customer service workflows at production scale. They designed the orchestration, evaluation, monitoring, and enterprise integrations, helping drive 70-80% automation and ~99% reliability across thousands of weekly interactions and millions of annual requests.”