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Vetted Machine Learning Professionals
Pre-screened and vetted.
Sriman Ujhanthachhen
Intern Software Engineer specializing in AWS cloud architecture and GenAI systems
“AWS Solutions Architect intern who advised customers on securing a multi-tenant LLM-based SaaS, including isolation strategy tradeoffs and production guardrails against prompt injection. Has experience investigating a prompt-injection incident using logs/traces and TTP-style documentation, and designing scalable SDK/agent integrations via asynchronous worker architecture with prompt versioning.”
Irfan Shaik
Mid-level AI Software Engineer specializing in risk and fraud detection
“AI/software engineer with experience at Visa building a real-time transaction fraud/risk scoring microservice in the card authorization path (Python, Kafka, Kubernetes on AWS) with strict 120–150ms latency constraints and reason-code outputs for downstream decisioning. Owns ML backend end-to-end (data/feature engineering, model training, deployment) and has demonstrated production reliability work including latency spike mitigation, SLO-based observability, drift monitoring, and safe fallbacks to rule-based decisions.”
Deepthi Mundarinti
Mid-level Generative AI Engineer specializing in decision intelligence and RAG for regulated enterprises
“Healthcare GenAI engineer who built a HIPAA-compliant, auditable RAG-based claims decision support system at Molina Healthcare, processing 3M claims and delivering major impact (48% faster manual reviews, 43% higher decision accuracy). Deep hands-on experience with LangChain orchestration, vector search (ChromaDB/FAISS), embedding fine-tuning, and safety controls (confidence scoring, rule validation, human-in-the-loop escalation) for clinical workflows.”
Avinash Pancheneni
Mid-level Machine Learning Engineer specializing in fraud detection and LLM applications
“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”
Wei Jiang
Junior Machine Learning Engineer specializing in MLOps and statistical modeling
“Integration engineer at ES Foundry who led deployment of ELsentinel, a production EL image-based solar cell quality monitoring system using a Swin Transformer classifier (>0.8 F1 across 15+ classes) plus a live real-time prediction dashboard. Strong in solving messy labeling/data-quality problems with process-team collaboration and shipping ML systems despite limited compute/infrastructure.”
John Chen
Junior Full-Stack & Data Scientist specializing in ML/NLP and analytics products
“Built and deployed profitprops.io, a sports betting player-props prediction product using ML/AI. Implemented backend APIs with FastAPI/Express.js and Supabase, trained models on AWS GPU (P3) using Docker + RAPIDS, and set up CI/CD with GitHub Actions while working around cost constraints and data-collection hurdles (EC2 proxy rotation/rate limits).”
Monish Sri Sai Devineni
Mid-level Machine Learning Engineer specializing in financial AI, NLP, and MLOps
“AI/ML engineer with experience at Accenture and Morgan Stanley, building production LLM systems (GPT-3 summarization) and finance-focused ML models (credit risk and trading anomaly detection). Combines MLOps depth (Docker/Kubernetes, AWS SageMaker/Glue/Lambda, MLflow, A/B testing, drift monitoring) with practical domain adaptation techniques like few-shot prompting and RAG/knowledge-base integration.”
Shao Rong Su
Senior AI Research Engineer specializing in LLM agents and predictive maintenance
“At Delta Electronics, partnered with automotive firmware teams to productionize an LLM-based coding assistant for identifying safety standard violations and generating bug-fix guidance. Built an agentic workflow with stepwise context extraction, similarity search, and a separate judge model for scoring reasoning/retrieval, and drove internal adoption through pain-point discovery and tailored technical demos using real firmware code.”
Joseph Herbst
Executive Technology Leader specializing in IoT, PoE building automation, and semiconductors
“Engineering/exec leader (CEO experience) who built a lighting controls and building automation platform from scratch, choosing an Ethernet/PoE and Node.js web-centric architecture that anticipated market shifts and later became an industry pattern. Known for rigorous vendor QA (test-to-failure) and for delivering high-profile deployments (e.g., Black Desert Resort, Resorts World Villa 66) while leading globally distributed engineering teams.”
Vaibhav Sharma
Mid-level Software Engineer specializing in AI/ML and data platforms
“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”
Alexey Elkin
Junior Full-Stack Software Engineer specializing in cloud analytics and web applications
“LLM/agentic workflow engineer with hands-on experience turning demo-grade LLM analytics into production-ready features by tackling tail latency, observability, and cost/reliability controls. Strong at diagnosing real-time customer incidents via trace-driven debugging across retrieval, tool calls, retries, and prompt/version metadata, and frequently partners with sales as a technical lead to de-risk enterprise pilots through transparent failure-mode walkthroughs.”
Rifat Khan
Mid-level Mechanical/Aerospace Engineer specializing in scientific computing, CFD, and ML systems
“Robotics/control-focused engineer who built and validated a series elastic actuator control stack end-to-end (dynamic modeling, torque/position control, simulation, and experimental real-time debugging on hardware). Deep simulation background (OpenFOAM/COMSOL/Abaqus) and practical reproducibility tooling (Docker/CI), with conceptual ROS/ROS2 knowledge and confidence ramping into ROS-based stacks.”
Kanaka Chalam Volety
Staff DevOps/SRE Engineer specializing in AWS, Kubernetes, and GitOps
“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”
Jackson Dike
Entry-Level Software Engineer specializing in Machine Learning and AI
“Master’s-level candidate with an academic project portfolio, including ownership of a Python-based video game recommendation system using unsupervised clustering. Has hands-on experience designing the system approach and validating recommendation quality with test cases, plus teaching assistant experience instructing Git/GitHub workflows; limited exposure to Kubernetes, GitOps, and large-scale infrastructure.”
Pranav Chand
Senior AI/ML Engineer specializing in Generative AI and LLM platforms
“Backend engineer focused on multi-tenant enterprise AI personalization and recommendation platforms, combining ML/LLM intent extraction with deterministic policy guardrails for compliance and auditability. Has hands-on AWS experience (ECS/Lambda/DynamoDB/S3) and led a careful DynamoDB single-table migration using dual write/read, canary + feature-flag rollouts, and strong observability/security (JWT/OAuth2, RBAC, Postgres RLS).”
pavan kalyan padala
Mid-level Data Scientist specializing in predictive and generative AI
“AI/ML engineer with production LLM experience in regulated financial services (J.P. Morgan Chase), building a customer response engine to automate first-contact resolution while addressing privacy, bias, compliance, and scale. Strong MLOps/orchestration background (Airflow, Docker/Kubernetes, AWS Step Functions, Azure ML/SageMaker) plus proven ability to integrate with legacy systems and drive stakeholder adoption through dashboards, auditability, and training.”
Harshavardhan Reddy
Mid-level AI/ML Data Scientist specializing in NLP, computer vision, and risk analytics
“ML/AI engineer with Capital One experience building production-grade customer segmentation and fraud detection systems combining NLP (transformers) and anomaly detection. Strong MLOps and orchestration background (PySpark ETL, MLflow, Airflow, Docker/Kubernetes, Azure ML) with real-time monitoring/alerting and performance optimizations like quantization and caching, plus proven ability to deliver business-facing insights through Power BI/Tableau for marketing stakeholders.”
Sankalp Tiwari
Mid-Level Software Engineer specializing in backend microservices and FinTech data pipelines
“Backend engineer at Goldman Sachs who built LLM-powered reconciliation/reporting services and high-throughput Kafka pipelines (8M+ events/day). Strong in production-grade Python/FastAPI microservices on Kubernetes with GitOps-style CI/CD, plus experience migrating legacy reporting/settlement services onto an internal Kubernetes platform using shadow deployments and gradual cutovers.”
Aditya Jaiswal
Intern Software Engineer specializing in cloud, DevOps, and applied AI
“Full-stack engineer with startup ownership experience (Aiir) building 15+ TypeScript/Go microservice APIs on GCP Cloud Run with Kafka-based async event streaming and React CRM integrations for billing/analytics. Strong post-launch operator who tuned Oracle performance (partitioning/indexing/query optimization) and validated a 23% retrieval-time reduction via AWR, and has a quality/DevSecOps mindset (94% Pytest coverage, GitHub Actions, SonarQube, Twistlock, CloudWatch) including migrating 18+ production CI/CD pipelines.”
Utkarsh Mittal
Intern Data Scientist specializing in computer vision and LLM agents
“Software engineering candidate with hands-on experience building and shipping LLM agents: created a production AI enrichment/coding agent at Covalent Metrology using Apollo.io + OpenAI, and built a Mistral hackathon router that dynamically selects among models to reduce token cost while maintaining quality. Also developed a real-time financial margin analysis agent that emails actionable insights and iterated on reliability issues (e.g., fixing misrouted emails, improving news relevance filtering).”
Bhavyasree Chinthala
Mid-level Data Engineer specializing in cloud data pipelines and real-time streaming
“Data engineer with PNC Bank experience owning high-volume financial transaction pipelines end-to-end (Kafka/REST ingestion through Spark/Glue transformations to Redshift serving) for risk and fraud analytics. Built strong reliability and data quality practices (Great Expectations, reconciliation, Airflow alerting, idempotent retries, incremental/windowed processing), reporting 40% ingestion efficiency gains and ~99.9% data accuracy.”
Srushti Milind Jamsandekar
Mid-Level Software Developer specializing in backend, cloud, and GenAI
“Full-stack engineer with fintech and AI feature experience who shipped an AI-powered project summary module in Next.js (App Router + TypeScript) with secure server-side fetching and route handlers to a FastAPI backend, then owned monitoring and performance fixes in production. Demonstrated measurable UX wins (30% faster dashboard loads) and strong backend fundamentals (Postgres indexing/EXPLAIN ANALYZE, SQS-orchestrated idempotent reconciliation workflows with DLQs and retries).”
Suloni Praveen
Entry-Level Software Engineer specializing in data engineering and ML systems
“Built an end-to-end Next.js/TypeScript LLM-based scientific PDF analyzer using local Ollama/Llama inference to prioritize privacy and cost, producing structured research artifacts (e.g., authors/methods/findings) with ~92% extraction accuracy. At Qualtrics, helped replace a batch pipeline with a real-time, low-latency ML inference service (Python/Go on Kubernetes) using Redis caching, Grafana-based observability, and graceful fallbacks to protect UX during failures.”
“PhD-led research engineer who has shipped LLM-powered agents for automated knowledge extraction from STEM textbooks/papers into a graph database, reporting a 90% accuracy improvement and major reductions in manual curation time. Also built an end-to-end multi-agent news aggregation/sentiment pipeline using the Agno framework with Pydantic-structured outputs, retries, and monitoring, and has experience processing messy SEC filings.”