Machine learning engineer
12 hours ago
Skills: Python, Tensor Flow, Py Torch, scikit-learn, Pandas; experienced in AI and ML model development, integration, deployment; familiar with Docker, Kubernetes, CI/CD tools, AWS/Azure/GCP; strong in data structures, algorithms, system design; and knowledgeable in SQL, Postgre SQL, and No SQL databases.
Responsibilities
Model Development and Integration: Design, implement, test, integrate and deploy scalable machine learning models, integrating them into production systems and APIs to support existing and new customers.
Experimentation and Optimisation: Lead the design of experiments and hypothesis testing for product feature development; monitor and analyze model performance and data accuracy, making improvements as needed.
Cross-Functional Collaboration: Work closely with cross-functional teams across India and the US to identify opportunities, deploy impactful solutions, and effectively communicate findings to both technical and non-technical stakeholders.
Mentorship and Continuous Learning: Mentor junior team members, contribute to knowledge sharing, and stay current with best practices in data science, machine learning, and AI.
Qualifications
Education: Bachelors or Masters in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
Experience:
4-7 years of experience building and deploying machine learning models.
Strong problem-solving skills with an emphasis on product development.
Experience operating and troubleshooting scalable machine learning systems in the cloud.
Technical Skills:
Programming and Frameworks: Proficient in Python with experience in Tensor Flow, Py Torch, scikit-learn, and Pandas; familiarity with Golang is a plus; proficient with Git and collaborative workflows.
Software Engineering: Strong understanding of data structures, algorithms, and system design principles; experience in designing scalable, reliable, and maintainable systems.
Machine Learning Expertise: Extensive experience in AI and machine learning model development, including large language models, transformers, sequence models, causal inference, unsupervised clustering, and reinforcement learning. Knowledge of prompting techniques, embedding models and RAG.
Innovation in Machine Learning: Ability to design and conceive novel ways of problem solving using new machine learning models.
Integration, Deployment, and Cloud Services: Experience integrating machine learning models into backend systems and APIs; familiarity with Docker, Kubernetes, CI/CD tools, and Cloud Services like AWS/Azure/GCP for efficient deployment.
Data Management and Security: Proficient with SQL and experience with Postgre SQL; knowledge of No SQL databases; understanding of application security and data protection principles.
Methodologies and Tools:
Agile/Scrum Practices: Experience with Agile/Scrum methodologies.
Project Management Tools: Proficiency with Jira, Notion, or similar tools.
Soft Skills:
Excellent communication and problem-solving abilities.
Ability to work independently and collaboratively.
Strong organizational and time management skills.
High degree of accountability and ownership.
Preferred Skills
Experience with big data tools like Hadoop or Spark.
Familiarity with infrastructure management and operations lifecycle concepts.
Experience working in a startup environment.
Contributions to open-source projects or a strong Git Hub portfolio.
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