Hire the Best Modevity SPARK Specialists

I build AI applications, data pipelines, analytics solutions, computer vision systems, and automation tools using Python. My work spans Generative AI, Retrieval-Augmented Generation (RAG), data engineering, big data, statistical analysis, machine learning, computer vision, dashboards, and backend development. I can help take a project from raw data, documents, images, or business workflows to a working system, automated pipeline, dashboard, API, or deployed AI solution. What I can help you with: Generative AI and LLM applications Retrieval-Augmented Generation (RAG) systems AI agents, chatbots, and knowledge assistants Python automation and API integrations FastAPI backend development Data engineering and ETL pipelines PySpark, Apache Spark, and Databricks Big data processing and performance optimization SQL data modeling and database workflows Data cleaning and exploratory data analysis Statistical analysis and KPI reporting Tableau and Power BI dashboards Machine learning and predictive modeling Computer vision and image processing YOLO object detection and OCR OpenCV-based automation Document processing and intelligent search Recent projects include an AI telecom engineering copilot that analyzes KPI datasets and technical documentation using RAG, a PySpark and Databricks platform processing 23M+ financial records, an LLM-powered WhatsApp business automation assistant, a machine-learning cellular network analytics system, and computer vision pipelines for OCR and image analysis. Technical stack: Python, SQL, Pandas, PySpark, Databricks, Apache Spark, Scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, RAG, LLM APIs, FastAPI, OpenCV, YOLO, Docker, Git, GitHub Actions, Supabase, PostgreSQL, REST APIs, Tableau, Power BI, and Linux. My engineering background also includes telecommunications, IoT, networking, and statistical signal/data analysis, which helps me work effectively on technical and domain-specific projects rather than only generic software applications. I’m available for projects involving AI systems, data engineering, analytics, computer vision, automation, and Python backend development.

I'm a Senior Data Engineer with 4.5+ years of experience building scalable, cloud-native data platforms that turn raw data into reliable, business-ready insights. I've delivered enterprise solutions across banking (NAB), healthcare (Molina), and CPG (PepsiCo), specializing in end-to-end pipeline architecture, data modeling, and cloud migrations. What I bring to your project: 🔹 Cloud Data Engineering – Deep expertise in Azure (Databricks, Data Factory, Synapse) and AWS (EMR, Glue, S3, RedShift), with hands-on migration experience from on-prem and Teradata to cloud. 🔹 Pipeline Architecture & ETL – I design and build robust ingestion frameworks handling batch, incremental, and real-time data (Event Hub, Kafka) across formats like JSON, CSV, Parquet, and fixed-width files. 🔹 Data Modeling & Warehousing – Skilled in dimensional modeling, Data Vault, star/snowflake schemas, and silver/gold layer design. I've modeled 50+ tables across Oracle Fusion, SAP S/4, and healthcare domains. 🔹 Transformation & Orchestration – I translate complex business rules into DBT models, orchestrate workflows with Apache Airflow or AutoSys, and automate CI/CD via Jenkins and Azure DevOps. 🔹 Performance & Governance – I tune PostgreSQL and Spark jobs, implement data quality checks, reconciliation frameworks, and ensure compliance with data governance standards. 🔹 Generative AI & MLOps – Databricks-certified in Generative AI, with experience integrating MLflow for experiment tracking and building LLM-based automation using OpenAI and LangChain. Tech Stack: Python | SQL | Scala | Apache Spark | DBT | PostgreSQL | Snowflake | Airflow | Databricks | Azure | AWS | Git | Jenkins | MLflow | Power BI Certifications: Databricks Certified Data Engineer Professional | Azure Data Engineer (DP-203) | Snowflake SnowPro Core | Fabric Analytics Engineer (DP-600) | Generative AI Engineer Associate Whether you need a production-grade pipeline, a cloud migration, or a well-modeled data warehouse, I deliver clean, documented, and scalable solutions — on time and with clear communication. Let's discuss your project!

I build production AI agents and RAG systems that connect LLMs to private documents, databases, APIs, and enterprise workflows. My strongest work is turning an AI prototype into a reliable application with evaluation, security, deployment, and monitoring. I have 6 years of experience delivering production-grade AI and data systems in Fortune 500 retail, consulting, financial-data, and cybersecurity environments. SELECTED RESULTS • Built a real-time multi-agent cybersecurity platform that improved incident-response speed by 40%. • Delivered an image-to-data pipeline with 95%+ accuracy across complex document types. • Built natural-language-to-SQL assistants that eliminated manual query writing and enabled non-technical teams to access live business data. • Developed enterprise document and workflow automation using private data, APIs, cloud services, and human approval controls. WHAT I BUILD • Enterprise AI agents and multi-agent workflows using LangGraph, LangChain, AutoGen, and LlamaIndex • Production RAG systems over documents, databases, and business knowledge • Document intelligence, extraction, classification, and structured-output pipelines • Natural-language SQL and analytics assistants • MCP, API, ERP, and business-workflow integrations • Cloud deployment and production hardening on Azure and AWS DELIVERY I can own the complete lifecycle: requirements discovery, architecture, prototype, retrieval and data layer, API backend, evaluation, deployment, monitoring, and support. CORE STACK Python, FastAPI, LangGraph, LangChain, LlamaIndex, AutoGen, Azure OpenAI, AWS Bedrock, OpenAI, Claude, Gemini, Pinecone, Weaviate, PostgreSQL, Docker, Kubernetes, Databricks, and Kafka. WHY CLIENTS HIRE ME • Production systems, not isolated prompt demos • Full-stack AI ownership from architecture through deployment • Clear communication, documented decisions, and realistic estimates • Enterprise integration experience with private data and controlled workflows Building an AI agent, RAG system, document-intelligence workflow, or enterprise LLM application? Send me the problem, current systems, and desired outcome. I will propose the smallest reliable path to production.

Top Rated Plus optical engineer ($200K+ earned, 100% JSS) who now pairs 25 years of lens, laser, and illumination design with modern AI/ML — so your optical product gets designed AND its imaging, inspection, or design-optimization intelligence built by one engineer. What I do: • Optical & lens design — imaging, laser, LED/illumination systems in Zemax OpticStudio, concept to high-volume production • Computer Vision & image processing — hardware simulation for the software AI/ML integration • Physics-informed ML & inverse design — surrogate models and ML-accelerated optimization for Zemax/Lumerical/FRED design loops • End-to-end delivery — in-house 3D printing, CNC, laser scanning, and lighting measurement, plus a vetted prototype-to-production fabrication network How I work: fixed-price or hourly with a clear, milestone-based scope. Inventive, confidential, and embedded alongside your product team. No surprises. Ideal for teams building cameras, sensors, EO/IR systems, medical/endoscopic optics, or inspection hardware who need both the optics and the AI in one trusted US-based expert. Message me with your goal and I'll send a short written plan and fit check.

I build specialized ML models for time series data, paired with conversational AI interfaces that let users query their data, generate forecasts and detect anomalies in natural language — replacing static dashboards with direct, interactive access to the data and models. PhD in statistics, 15+ years in applied ML, with peer-reviewed publications. AWS-certified ML specialist and generative AI developer. Deliverables: - ML models for time series forecasting, anomaly detection, clustering and classification - AI agents with Amazon Bedrock AgentCore and Strands Agents - Serverless ML solutions on AWS Lambda with Amazon API Gateway endpoints - Custom Amazon SageMaker containers for training and inference - Integration with data backends such as ClickHouse, PostgreSQL and S3 Tools & frameworks: Python · SQL · Docker · PyTorch · TensorFlow · scikit-learn · XGBoost · LightGBM · CatBoost · Amazon Bedrock (AgentCore, Strands Agents) · Amazon SageMaker (AutoPilot, BYOC) · AWS Lambda · Amazon API Gateway · ClickHouse · PostgreSQL · RDS · S3

Senior AI/ML Engineer with 7+ years of experience taking AI and ML systems from architecture through production. I combine hands-on Python engineering with RAG, agent orchestration, MLOps, data platforms, and cloud deployment across GCP and AWS. My Upwork record includes $20K+ earned across 20 jobs. What I can deliver: • Production RAG: ingestion, hybrid retrieval, reranking, citations, grounding checks, abstention, evaluation, and latency/cost traces • Reliable LangGraph agents: persistent state, structured outputs, human approval, escalation, retries, timeouts, and circuit breakers • Secure MCP and tool gateways: OIDC, RBAC, least-privilege data access, PII masking, query controls, and audit logging • Python/FastAPI services, Docker, CI/CD, Kubernetes, Cloud Run, Vertex AI, BigQuery, Dataflow, PySpark, and AWS Bedrock • Traditional ML where it is the better fit: forecasting, NLP, computer vision, classification, feature pipelines, model serving, and monitoring Public engineering evidence includes four tested reference implementations: a RAG Quality Workbench, a reliable LangGraph support agent, a secure multi-connector MCP gateway, and an AWS Bedrock RAG API. These demonstrate production-minded architecture without exposing client source code or data. My production background includes retail forecasting and hierarchical ML systems, PySpark feature pipelines, TensorFlow training/inference, policy intelligence and semantic search, Vertex AI embeddings, Cloud SQL, Dataproc, BigQuery, and multi-environment delivery workflows. I have worked independently and with Open Insights, Capgemini, and Hughes Systique. Google Cloud Professional credentials earned: Cloud Architect and Machine Learning Engineer. I start by confirming the business outcome, data and integration constraints, acceptance criteria, security boundaries, and measurable evaluation plan. I then deliver in transparent milestones with tested code, deployment guidance, observability, and maintainable handover documentation. Send me your current problem, stack, constraints, and definition of done, and I will propose a practical path to production.