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Job Title: Senior AI/MLOPs Engineer
Job Summary:
We are seeking an experienced Machine Learning Engineer with a strong background in AI and ML frameworks, who is passionate about developing innovative solutions and advancing our technology stack. The ideal candidate will have hands-on experience with Python, machine learning models, NLP, and cloud platforms, with a deep understanding of MLOps and infrastructure automation.
Key Responsibilities:
- Programming Expertise:
- Develop and maintain Python-based applications and frameworks using tools like PySpark, Pandas, and NumPy.
- Build RESTful APIs using Python frameworks such as FastAPI, Flask, or Django for robust and scalable services.
- Machine Learning & AI:
- Lead the design, training, and deployment of AI/ML models, applying your 5–8 years of experience.
- Proficient in popular ML frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn.
- Work with neural network architectures like Ensemble Models, SVM, CNN, RNN, and Transformers to create cutting-edge solutions.
- NLP & Generative AI:
- Implement NLP solutions using industry-standard libraries like NLTK, SpaCy, Hugging Face, and Gensim.
- Build and deploy generative AI models and text analytics solutions.
- MLOps & Cloud Infrastructure:
- Manage the end-to-end ML lifecycle using AWS, particularly SageMaker for model training, deployment, and monitoring.
- Build scalable and automated MLOps pipelines on cloud platforms such as AWS, Azure, or GCP.
- Leverage MLOps tools like MLflow, SageMaker Pipelines, Airflow, and Kubeflow for smooth and efficient workflows.
- Experience in containerization (Docker, Kubernetes) and microservices architecture.
- Feature Engineering & Data Management:
- Lead efforts in data preprocessing, feature extraction, and transformation for ML pipelines.
- Ensure optimal data handling using SQL and NoSQL databases such as MongoDB, PostgreSQL, or Neo4j.
- DevOps & Automation:
- Automate workflows for model deployment and lifecycle management, ensuring seamless integration.
- Utilize Infrastructure-as-Code tools like Terraform or CloudFormation for infrastructure automation.
- Build and manage CI/CD pipelines to deploy machine learning models efficiently into production.
Required Skills & Experience:
- Bachelor’s degree or higher in Computer Science, Information Technology, Data Science, or a related field.
- 5–8 years of hands-on experience in AI/ML development and model deployment.
- Proven track record in implementing machine learning solutions using state-of-the-art frameworks.
- Expertise in natural language processing, generative AI, and NLP tools.
- Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools.
- Strong knowledge of data management, feature engineering, and database technologies.
- Familiarity with containerization, microservices, and DevOps principles.
- Exceptional problem-solving skills and the ability to work collaboratively in a fast-paced environment.
Soft Skills:
- Strong communication skills to engage with cross-functional teams.
- Proactive attitude with a keen interest in continuous learning and problem-solving.
Key Skills
Ranked by relevance
machine learning
cloud
mlops
python
aws
ai
containerization
microservices
natural language processing
cloudformation
restful apis
kubernetes
postgresql
tensorflow
terraform
kubeflow
fastapi
pytorch
django
docker
devops
pandas
mlflow
nosql
flask
keras
cicd
sql
gcp
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- Posted
- Feb 06, 2025
- Type
- Full-time
- Level
- Mid-Senior
- Location
- Germany
- Company
- Smartedge Solutions
Industries
IT Services
IT Consulting
Telecommunications
Financial Services
Categories
Engineering
Information Technology
Management
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