Senior Applied AI Engineer
New
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- Minimum of 3 years
- Required Skills
- AWSDockerPostgreSQLPythonGCPKubernetesMicrosoft AzurePyTorchTensorflowscikit-learnLLMMLOps
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related field.
- Minimum of 3 years of experience as an AI Engineer, Machine Learning Engineer, Applied AI Engineer, or similar role.
- Strong experience training, fine-tuning, deploying, and maintaining machine learning models in production environments.
- Advanced proficiency in Python and hands-on expertise with machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn.
- Experience managing production ML systems and building scalable AI architectures.
- Hands-on experience with cloud platforms such as AWS, Google Cloud Platform, and/or Microsoft Azure.
- Familiarity with managed machine learning services including Amazon SageMaker, Vertex AI, or similar platforms.
- Strong understanding of API design principles, distributed systems, and scalable backend architectures.
- Practical experience implementing MLOps practices, including CI/CD pipelines, model monitoring, observability, and automated deployment workflows.
- Experience working with Docker, Kubernetes, PostgreSQL, and modern data infrastructure technologies.
- Strong knowledge of large language models, retrieval-augmented generation (RAG) architectures, embeddings, and vector databases.
- Excellent analytical thinking, problem-solving abilities, and communication skills, with strong written and verbal English proficiency.
Responsibilities
- Own the end-to-end development and delivery of production-grade AI and machine learning systems, from research and experimentation to deployment and monitoring.
- Train, fine-tune, optimize, and maintain machine learning models, including large language models and open-weight AI models.
- Build and manage scalable data processing, training, inference, and evaluation pipelines to support production AI workloads.
- Improve model performance across key metrics such as accuracy, latency, reliability, scalability, and cost efficiency.
- Implement MLOps best practices, including CI/CD pipelines, automated retraining processes, model monitoring, and governance frameworks.
- Develop evaluation methodologies, benchmark datasets, and quality assurance mechanisms to ensure model robustness and performance.
- Design and maintain scalable APIs and backend services that expose AI capabilities to internal and customer-facing applications.
- Collaborate closely with product, frontend, backend, and infrastructure teams to integrate AI solutions into enterprise workflows.
- Monitor production environments, troubleshoot issues, and continuously enhance both model and infrastructure performance.
- Research and evaluate emerging AI techniques, tools, and frameworks to drive innovation and improve product capabilities.
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