ML Architect
- Quinnox
- 11 - 21 years
- Bengaluru
- 5 months ago
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Job Description
The Lead ML Architect will own the end-to-end design, development, and deployment of advanced ML and AI platforms, driving the architecture and technical direction for high-performance, scalable machine learning solutions. The role demands extensive hands-on expertise in building machine learning platforms, leveraging AWS SageMaker, Python-based frameworks, and parallel computing for efficient large-scale data processing.
Key Responsibilities:
- Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms.
- Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies.
- Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models.
- Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems.
- Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms.
- Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization.
- Lead and mentor a high-performing team of ML engineers, promoting best practices, technical excellence, and continuous learning.
- Ensure compliance with model governance standards, data security best practices, and regulatory requirements (e.g., GDPR, HIPAA).
- Implement robust monitoring, alerting, and logging strategies to ensure consistent performance and rapid incident response for ML systems in production.
- Continuously optimize the performance, scalability, and cost-effectiveness of deployed ML models.
- Partner closely with product management and executive stakeholders to ensure alignment of ML platform development with strategic business objectives.
- Stay abreast of latest advancements in ML and AI technologies, proactively applying new techniques and tools to deliver superior outcomes.
Day-to-Day Activities:
- Design, build, and maintain scalable ML platform infrastructure and tooling.
- Develop optimized machine learning models, leveraging advanced supervised and unsupervised techniques.
- Rapidly prototype and iterate on proof-of-concepts and transition successful prototypes into enterprise-grade solutions.
- Conduct comprehensive reviews and present findings clearly to technical and non-technical stakeholders.
- Continuously perform horizon scanning and research emerging ML trends, tools, and methodologies.
Knowledge and Skills:
Essential:
- Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker.
- Strong proficiency in Python and its ecosystem (e.g., TensorFlow, PyTorch, scikit-learn).
- Extensive hands-on experience with parallel computing frameworks and distributed processing.
- Proficient in SQL, ETL, data warehousing, and data modeling techniques.
- Thorough understanding of statistical analysis, predictive modeling, and data mining methodologies.
- Proven capability of deploying machine learning models into production at scale.
- Familiarity with CI/CD pipelines, containerization (Docker), and version control (Git).
Preferred:
- Experience with Kubernetes orchestration systems.
- Familiarity with Snowflake data warehousing and NoSQL databases like MongoDB.
- Hands-on experience with messaging systems (e.g., Kafka).
- Exposure to distributed computing frameworks such as Hadoop, Spark, and Ray.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: NLP / DL Engineering / ArchitectEmployement Type: Full timeContact Details:
Company: QuinnoxLocation(s): Bengaluru+ View Contact
Keyskills: ML Architect MLOPS AWS Sagemaker
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