Associate Principal Engineer, Machine Learning
New
IndiaFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 9+ years
- Required Skills
- DockerPythonSQLKubeflowKubernetesMachine LearningMLFlowDeep LearningMLOps
Requirements
- 9+ years of experience in machine learning, AI engineering, or data science roles with strong architectural exposure
- Proven experience delivering production-grade ML solutions across NLP, computer vision, or AI-driven systems
- Strong expertise in AI/ML architecture design on cloud and big data environments
- Advanced programming skills in Python, with experience using libraries such as Pandas, NumPy, and Scikit-learn
- Strong hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX
- Solid understanding of statistical methods and their application in real-world ML problems
- Experience working with SQL and large-scale data processing systems
- Strong knowledge of MLOps practices and tools such as MLflow, Kubeflow, Docker, and Kubernetes
- Experience designing and deploying AI agents and multi-agent systems
- Strong understanding of LLMs, foundation models, prompt engineering, and RAG-based architectures
- Experience applying responsible AI principles and ethical AI frameworks
Responsibilities
- Design and architect end-to-end machine learning and AI solutions aligned with business and technical requirements
- Translate complex business use cases into scalable, production-ready ML system designs and technical architectures
- Lead design decisions across data pipelines, model training, deployment, and monitoring in cloud-based environments
- Define AI/ML architecture standards, guidelines, and best practices including NFRs such as scalability, security, and performance
- Develop and review architecture and design documentation, ensuring clarity for engineering implementation teams
- Evaluate and select optimal ML approaches, tools, frameworks, and technologies based on client requirements
- Design and guide development of AI/ML solutions across NLP, computer vision, and generative AI domains
- Build and oversee implementation of MLOps pipelines using tools such as MLflow, Kubeflow, Docker, and Kubernetes
- Design and deploy AI agents and multi-agent systems for autonomous or semi-autonomous decision-making
- Lead proof-of-concept initiatives to validate architectures, frameworks, and emerging AI technologies
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