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Candidates local to TX are preferred.
Hello,
My name is Adarsh Nandi, and I work as a Technical Recruiter for K-Tek Resourcing.
We are searching for Professionals below business requirements for one of our clients. Please read through the requirements and connect with us in case it suits your profile.
We are searching for Professionals below business requirements for one of our clients. Please read through the requirements and connect with us in case it suits your profile.
Please see the Job Description and if you feel Interested then send me your updated resume at [email protected].
Job Title: Lead with AI/ML/LLM and MLOPS Engineer
Location: Austin, TX
Duration: Long Term
Job Description:
Technology stack that team will be working on (MLOPS, ICEberg, AWS and front end Lightweight NextJS are important)
Tech stack details:-
• Backend: Python (Django, FastAPI)
• Unit/integration testing (pytest)
• Asynchronous job scheduling (Celery)
• Adherence to PEP8 and PEP257 coding standards
• Frontend: ReactJS (Next.js)
• Machine Learning:
• Python ML & DS ecosystem (PyTorch, XGBoost, scikit-learn, pandas, numpy)
• Model interpretability & explainability frameworks (SHAP)
• ML pipelines & workflow automation (MLflow, Airflow)
• Infrastructure: AWS (EC2, S3, EKS, SageMaker)
• Infrastructure as Code (IaC) using Terraform and Ansible
• Proper IAM role-based access controls
• Network security best practices (VPC, security groups, private subnets)
• MLOps:
• Model training, monitoring, & deployment (MLflow, AWS SageMaker, AWS Airflow)
• Data labeling & versioning (Label Studio, DVC
• CI/CD & Monitoring:
• Automated linting, test & deployment: Jenkins
• Telemetry & Monitoring: Prometheus, Grafana, AWS CloudWatch
• Containerization & Orchestration: Docker, Kubernetes, Helm
• Additional technologies as needed
Tech stack details:-
• Backend: Python (Django, FastAPI)
• Unit/integration testing (pytest)
• Asynchronous job scheduling (Celery)
• Adherence to PEP8 and PEP257 coding standards
• Frontend: ReactJS (Next.js)
• Machine Learning:
• Python ML & DS ecosystem (PyTorch, XGBoost, scikit-learn, pandas, numpy)
• Model interpretability & explainability frameworks (SHAP)
• ML pipelines & workflow automation (MLflow, Airflow)
• Infrastructure: AWS (EC2, S3, EKS, SageMaker)
• Infrastructure as Code (IaC) using Terraform and Ansible
• Proper IAM role-based access controls
• Network security best practices (VPC, security groups, private subnets)
• MLOps:
• Model training, monitoring, & deployment (MLflow, AWS SageMaker, AWS Airflow)
• Data labeling & versioning (Label Studio, DVC
• CI/CD & Monitoring:
• Automated linting, test & deployment: Jenkins
• Telemetry & Monitoring: Prometheus, Grafana, AWS CloudWatch
• Containerization & Orchestration: Docker, Kubernetes, Helm
• Additional technologies as needed
