Sr. AI Engineer
New
Based in IndiaFull-TimeSenior
Salary50,000 - 120,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- AWSDockerPythonSQLKubernetesMachine LearningSparkCI/CDMLOps
Requirements
- Bachelor’s degree in Computer Science, Machine Learning, Statistics, Physics, Engineering, or a related technical field
- 6+ years of experience in machine learning engineering with proven success deploying production-grade ML systems
- Strong expertise in Python; Golang experience is a plus
- Deep understanding of machine learning concepts including recommendation systems, pattern recognition, data mining, or AI systems
- Proven track record of building ML models from concept to production with measurable business impact
- Strong experience with AWS cloud services for ML model deployment and scaling
- Solid knowledge of MLOps practices, including Kubernetes, Docker, and CI/CD pipelines
- Experience with relational databases, SQL, and data warehouse systems
- Familiarity with data processing frameworks such as Spark or Kafka for real-time or large-scale data handling
- Strong experience with observability tools such as Prometheus, Grafana, or CloudWatch for ML systems
- Demonstrated technical leadership, including mentoring and owning end-to-end delivery
- Strong communication skills and ability to thrive in fast-paced, high-ownership environments
Responsibilities
- Design, build, and maintain scalable machine learning infrastructure on AWS, including training and deployment pipelines using services such as SageMaker
- Develop and deploy ML models for recommendation systems, fraud detection, credit risk, and personalization use cases
- Collaborate with product and engineering teams to rapidly build and iterate on AI-driven MVPs and production features
- Implement monitoring, logging, and alerting systems to ensure model performance, stability, and reliability in production
- Support end-to-end ML lifecycle operations, including debugging, optimization, and production incident resolution
- Enable cross-functional teams to leverage AI/ML capabilities, including generative AI solutions where applicable
- Design and scale ML architectures to support high growth, ensuring efficiency, cost optimization, and robustness
- Mentor engineers, conduct code reviews, and promote best practices in ML engineering and MLOps
- Stay current with advancements in machine learning, AI frameworks, and cloud technologies, driving continuous innovation
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