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I will build production ready mlops ai ml and llm solutions
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I'm an MLOps Engineer specializing in designing, deploying, and maintaining production-ready ML systems. I work with Python, FastAPI, Docker, Kubernetes, GitHub Actions, MLflow, Redis, AWS/Azure/GCP, ...
About this Gig
I'm an MLOps Engineer and Data Scientist with years of experience building production-grade, scalable, end-to-end MLOps pipelines. I'll deploy your AI/ML models at scale with experiment tracking, model versioning, automated retraining, and drift detection.
CI/CD Automation: GitHub Actions, Jenkins, ArgoCDreducing release cycles by 70% and deployment failures by 85%.
Containerization & Orchestration: Docker, Kubernetes (EKS/AKS/GKE), HPA, Istio, Helmachieving 40% smaller images and 99.9% availability.
ML Pipelines: Kubeflow, MLflow, Airflow, Feast, DVC, ZenMLreducing manual work by 85% and accelerating development by 50%.
Cloud Deployment: AWS (SageMaker, ECR, EKS, Lambda, API Gateway), Azure ML, GCP Vertex AI, Terraform for Infrastructure as Code.
Monitoring & Observability: Prometheus, Grafana, Evidently AI, DeepChecks, WhyLogs, PagerDutyachieving 75% faster MTTD.
Data Quality: Great Expectations, Pandera, Pydanticreducing data issues by 60% with 15 expectation suites.
NLP & LLMs: PyTorch, Hugging Face, LangChain, RAG, Fine-Tuning, LLaMA, VLLMachieving 89% sentiment accuracy.
Tech Stack: Python | SQL | TensorFlow |Scikit-learn | FastAPI | Redis
Let's deploy your AI with confidenc
Programming language:
Python
•
MATLAB
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SQL
•
MLflow
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Amazon SageMaker
Frameworks:
Scikit-learn
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Google ML Kit
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Keras
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PyTorch
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Panda
My Portfolio
FAQ
Q1: Do you build end-to-end MLOps pipelines with CI/CD automation?
Yes! I build complete MLOps pipelines from data ingestion and feature engineering to model training, deployment, and monitoring. I automate CI/CD using GitHub Actions, Jenkins, and ArgoCD with Docker and Kubernetes for production-ready deployments.
Q2: Can you deploy ML models on AWS, Azure, or GCP?
Absolutely! I deploy scalable ML systems on AWS (SageMaker, ECR, EKS, Lambda), Azure ML, and GCP Vertex AI using Terraform for Infrastructure as Code with 99.9% availability.
Q3: Do you work with LLMs, RAG, and Vector Databases?
Yes! I specialize in LLM solutions with LangChain, OpenAI API, RAG (Retrieval-Augmented Generation), and Vector Databases like Pinecone and ChromaDB for semantic search, document intelligence, and question answering.
Q4: How do you ensure model performance in production?
I implement monitoring with Prometheus, Grafana, Evidently AI, and DeepChecks for drift detection, performance monitoring, and alerting—achieving 75% faster MTTD (Mean Time To Detect).
Q5: Do you provide testing and quality assurance?
Yes! I implement unit testing, integration testing, and test automation with Pytest—achieving 81%+ test coverage. I also use Great Expectations and Pydantic for data validation and data quality assurance.
Q6: What tech stack do you use?
My stack: Python, FastAPI, PyTorch, TensorFlow, Scikit-learn, MLflow, DVC, Airflow, Docker, Kubernetes, Redis, PostgreSQL, Git, Linux, and Bash.
Q7: Can you handle large-scale predictions?
Yes! I build scalable systems handling 10K+ daily predictions with auto-scaling, load balancing, and real-time inference via FastAPI REST APIs.
Q8: Do you provide model versioning and rollback?
Absolutely! I implement model versioning, experiment tracking with MLflow, and model registry for seamless rollback and reproducible experiments.
Q9: What about security and authentication?
I implement authentication, rate limiting, PII detection, data privacy, secure document handling, and comprehensive logging for enterprise-grade security and compliance.
Q10: Can you build Document Intelligence Systems?
Yes! I build Document Intelligence solutions with OCR, document parsing, classification, extraction, NER (Entity Recognition), summarization, and semantic search using RAG and LLMs.
