Application Security Architect
Gurgaon · Staff/Principal
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Our tracked job listings for fa-ewjt-saasfaprod1-cx2 show limited recent activity.
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Job description
As an ML SME, you will serve as the go-to expert for machine learning initiatives, offering deep technical knowledge and practical experience to guide projects from concept to production. You will collaborate with data scientists, engineers, and business stakeholders to deliver scalable ML models that solve complex problems and create measurable business impact. This position requires a blend of technical expertise, thought leadership, and the ability to translate ML concepts into actionable strategies.
- Provide subject matter expertise in machine learning methodologies, frameworks, and tools.
- Lead the design, development, and deployment of ML models for diverse business applications.
- Partner with stakeholders to identify opportunities where ML can drive value.
- Ensure ML solutions are scalable, secure, and aligned with organizational standards.
- Mentor and coach data scientists, engineers, and analysts on ML best practices.
- Evaluate emerging ML technologies and recommend adoption strategies.
- Collaborate with cross-functional teams to integrate ML models into production systems.
- Establish and enforce governance, ethical AI practices, and model monitoring frameworks.
- Master’s or PhD in Computer Science, Machine Learning, Data Science, or related field.
- 8+ years of experience in machine learning, with proven expertise in model development and deployment.
- Strong proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and programming languages (Python, R, Java).
- Experience with cloud ML platforms (AWS SageMaker, Azure ML, GCP AI Platform).
- Solid understanding of data engineering, feature engineering, and MLOps practices.
- Demonstrated ability to lead complex ML projects and influence strategic decisions.
- Strong communication skills to explain technical concepts to non-technical stakeholders.
- Knowledge of ethical AI, bias mitigation, and responsible ML practices.
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