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Tech Stack
Tools & technologiesAWSAzureCloudPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Design, develop, and deploy AI/ML solutions, including predictive models, recommendation systems, and intelligent automation capabilities.
- Build and automate robust, scalable pipelines for data processing, model training, validation, deployment, and monitoring in production environments.
- Collaborate with data scientists, engineers, and product teams to translate business problems into AI-driven solutions.
- Analyse large and complex datasets to extract insights and develop data-driven intelligent systems.
- Own the end-to-end AI/ML lifecycle, including experimentation, model development, deployment, monitoring (e.g., drift detection), retraining, and optimization.
- Develop and integrate AI capabilities such as natural language processing (NLP), anomaly detection, or decision intelligence into applications.
- Implement and manage CI/CD pipelines and MLOps practices for AI/ML workflows, including testing, versioning, and model governance.
- Work closely with engineering and DevOps teams to build scalable infrastructure for training and inference (batch and real-time).
- Stay current with advancements in AI, ML, and Generative AI, and proactively identify opportunities to apply them within the platform.
- Continuously improve engineering practices, automation, and system performance.
Requirements
What you’ll need- 5+ years of experience in AI/ML engineering, data science, or a related field.
- Strong foundation in both Artificial Intelligence and Machine Learning concepts, including supervised/unsupervised learning and statistical modelling.
- Hands-on experience in building and deploying AI/ML solutions in production environments.
- Proficiency in Python and SQL for data processing and model development.
- Experience with ML frameworks/libraries such as TensorFlow, PyTorch, or Scikit-learn.
- Experience with Databricks or similar data platforms for building and deploying models.
- Familiarity with AI techniques (e.g., NLP, recommendation systems, or generative AI applications).
- Experience implementing MLOps practices using tools such as MLflow and CI/CD platforms (e.g., GitHub).
- Strong understanding of the full AI/ML lifecycle and production workflows.
- Familiarity with cloud-based data platforms such as Snowflake, AWS, or Azure.
- Strong problem-solving, analytical, and communication skills.
Benefits
Comp & perks- Remote work options
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
AI/ML solutionspredictive modelsrecommendation systemsnatural language processinganomaly detectionPythonSQLTensorFlowPyTorchScikit-learn
Soft Skills
problem-solvinganalytical skillscommunication skills