ML engineers & data scientists

Senior AI Engineer with 8+ years of experience building production machine learning and LLM applications. Works across Python/FastAPI backend development, retrieval-augmented generation, embeddings and semantic search, and workflow orchestration with LangGraph. Also delivers evaluation, fault handling, and observability for reliable inference and retrieval quality in production systems.

Data Engineer with 10+ years of experience designing and maintaining large-scale data platforms, ETL/ELT pipelines, and analytics solutions. Has processed TB-scale telecom datasets for analytics and machine learning use cases and implemented data quality and automation frameworks. Worked across telecom and utility domains, including mentoring engineers and partnering with data science and business stakeholders.

Junior software engineer focused on Python backend development and automation. Experience includes participating in SDLC activities such as development, testing, and maintenance, with version control using Git/GitHub. Built machine learning and computer vision projects using frameworks like TensorFlow/Keras, OpenCV, PyTorch, and NLTK.

LLM engineer focused on agentic RAG workflows and SQL-generating LLM tools at a senior level with pragmatic system design. The strongest proven skill is building agent orchestration and RAG pipelines, evidenced by langchain_langgraph_RND/langgraph/gemini/langchain_gemini_rag_sql.py and ai_resume_screener/agents/nodes.py. There is little to no evidence of custom model training, systematic experiment tracking, or production deployment automation in the public code.

A pragmatic ML engineer (mid level) focused on applied time-series forecasting and lightweight conversational tooling with a strong strength in feature engineering for domain problems. The clearest proven skill is time-series feature engineering and forecasting pipeline work, evidenced by the ERA5/met-mast merge, sin/cos wind-direction transforms, lag/rolling features and LightGBM training in the wind prediction notebooks. There is limited evidence of production-grade MLOps, experiment tracking, rigorous reproducibility practices, or research-level model innovation in public code.

A hands-on ML practitioner (middle level) focused on applied deep learning and computer vision prototypes with a strength in building end-to-end demo applications integrating detection models and lightweight language models.

Business-focused data analyst and BI analyst with 5+ years of reporting, data management, and operational analytics experience. Built and automated Power BI dashboards and Excel/SQL reporting for KPI tracking, customer analytics, and executive insights. Uses SQL, Excel, Power BI, and Python to clean, model, and transform data into decision-ready reporting.

Legal Technology & Automation Specialist with 5 years at Suzano. I administer DocuSign CLM, eSignature and IAM and build Python automations and API integrations (REST, JWT, JSON/XML, ServiceNow) for legal operations. I automated my own admin job with UiPath, a bot still running years later, which earned me two promotions. Founder of Krog, a multi-tenant WhatsApp SaaS (Meta Cloud API, Google Cloud Run, Supabase). Relocating to Canada with visa sponsorship.

A pragmatic ML engineer (middle level) who builds end-to-end classical ML workflows and utility computer vision tools using OpenCV and scikit-learn. The strongest proven skill is delivering simple MLOps-capable models and evaluation flows, demonstrated by the training script that logs to MLflow and the evaluate.py that computes metrics and saves plots. There is little evidence of advanced production hardening, automated tests, GPU or efficiency engineering, or research-grade model development in public code.

A practical LLM and multimodal engineer (senior-level estimate) who builds end-to-end RAG and classroom CV inference pipelines with Streamlit frontends. The strongest proven skill is integrating retrieval-augmented workflows and local LLM calls with concrete code in initialize_rag and generate_answer in Scholarship-AI-Assistant/app.py. There is limited evidence of custom research or distributed training, formal experiment tracking, or production-grade MLOps such as CI/CD and model versioning in public code.

Computer & Communications Engineering student with practical experience in Python, Git/GitHub, and AI/automation workflows. Built AI agent workflows and a custom RAG pipeline using LLM concepts and PyTorch, plus a computer-vision event detection and logging system with OpenCV. Also implemented embedded microcontroller automation logic using C++ state-machine design.

Data engineer with about 2+ years of experience building ETL/ELT data pipelines for reporting and analytics. Hands-on with SQL and Python, orchestrating workflows with Apache Airflow, and implementing data validation and reconciliation to ensure data quality. Completed API-driven data onboarding work using REST endpoints and Swagger, and integrated cloud components in data ingestion and processing.

Robotics-focused developer at a junior level specializing in real-time computer vision inference with OpenCV. The strongest proven skill is implementing a live YOLOv3-tiny inference demo as shown in object_detection/object_detection.py. There is no evidence of training pipelines, evaluation tracking, experiment management, or production-grade deployment in the public code.

A senior ML practitioner specializing in production image inference and computer vision pipelines. The strongest proven skill is building production inference pipelines with batching, Redis-backed queues, ONNX and PyTorch backends plus Prometheus observability as shown in app/worker.py and app/onnx_inference.py. There is limited evidence of rigorous statistical evaluation, systematic model selection, or enterprise MLOps automation such as data-versioning, CI pipelines, or formal cross-validation workflows.

Physics PhD with 5+ years of applied research experience in computational modeling and simulation of polymeric materials. Uses Python-based data analysis and computer-vision workflows to process molecular dynamics and impact experiment data, and communicates results through academic publications and presentations.

Backend API engineer (Middle) who builds focused, well-tested FastAPI demo services with careful input validation and sandboxed safety checks. The strongest proven skill is designing webhook-driven, idempotent event handling with HMAC verification and transactional updates as shown in onboarding.py and its tests. The work is demo-oriented and lacks evidence of production-grade scalability, migrations, distributed systems design, structured observability, or secret management practices.

A backend-focused engineer building web and real-time systems, assessed at an early senior level with strong practical skills in API and real-time ingestion design. The most proven skill is real-time behavior ingestion and analysis as implemented in backend/src/services/behaviorAnalysis.js and backend/src/socket/index.js which show windowed analytics, event emission and batched persistence. Public code lacks structured observability, formal load testing artifacts, and documented token lifecycle/advanced security controls.

Software engineer working as a working student, building and maintaining frontend components with Angular and TypeScript and developing full-stack features with Vue.js and NestJS. Experience includes database development with PostgreSQL and TypeORM and delivering features based on customer requirements. Completed an NLP project focused on transformer-based detection of AI-generated text.

Business analytics PGDM graduate with a BCA background, focused on turning data into insights using SQL, Python, and BI dashboards. Worked as a Solution Analyst handling incident triage for enterprise financial applications and coordinating with development teams. Completed internships and projects involving PostgreSQL data modeling, segmentation logic, and forecasting/dashboarding.

Applied ML engineer (middle level) focused on end-to-end supervised learning for tabular problems and simple desktop prototyping. The strongest proven skill is building and comparing ensemble and tree-based models for tabular prediction tasks as shown by the end-to-end pipelines and model experiments in Rent_It/Rent_it.ipynb and Segmint_It/segment it.ipynb. There is little evidence of production-grade MLOps, experiment tracking, custom model architectures, or rigorous software engineering practices in the public code.