Machine Learning Architect
1 day ago
Position : Machine Learning (ML) Architect
Experience : 5+ Years
Employment Type : Full-Time / Permanent
Job Overview :
We are seeking a Machine Learning Architect to design and lead the end-to-end architecture of scalable ML systemscovering data ingestion, feature engineering, model training, deployment, and continuous monitoring. The ideal candidate will bring deep expertise in MLOps, deep learning, and cloud-native ML platforms, enabling high-performing and secure AI solutions that align with strategic business objectives.
Key Responsibilities :
- Architect End-to-End ML Systems : Design scalable ML pipelines including data ingestion, preprocessing, model training, and inference workflows.
- Implement MLOps Practices : Define CI/CD, automated retraining, version control, and model governance frameworks for efficient ML lifecycle management.
- Deep Learning Architecture : Design and optimize deep learning solutions for complex data types, including audio, video, and multimodal inputs.
- GenAI and LLM Integration : Architect and deploy Generative AI and Large Language Model (LLM)-based solutions using modern frameworks.
- Cloud and Infrastructure : Lead deployment on AWS, Azure, or GCP using Kubernetes, Kubeflow, SageMaker, Vertex AI, or Azure ML.
- Cross-Functional Collaboration : Partner with data scientists, ML engineers, data engineers, and DevOps teams to ensure seamless integration of ML systems.
- Performance, Compliance, and Security : Ensure solutions meet organizational standards for security, scalability, and compliance.
- Technology Evaluation : Continuously evaluate new ML frameworks, tools, and best practices to enhance architecture efficiency and innovation.
Required Qualifications :
- Bachelors or Masters in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 5+ years of experience in designing and implementing ML/AI architectures and MLOps frameworks.
- Proven expertise in Deep Learning (CNNs, RNNs, Transformers), Generative AI, and LLM-based architectures.
- Proficiency in Python, TensorFlow, PyTorch, and MLFlow or equivalent tools.
- Strong knowledge of cloud-native ML ecosystems (AWS SageMaker, Azure ML, GCP Vertex AI).
- Solid understanding of data engineering, CI/CD pipelines, and container orchestration (Docker, Kubernetes).
- Experience with monitoring and observability tools for ML systems (e.g., Prometheus, Grafana, EvidentlyAI).
- Excellent problem-solving, communication, and leadership skills.
Preferred Skills :
- Experience with distributed training frameworks such as Horovod or DeepSpeed.
- Familiarity with vector databases (Pinecone, FAISS) and retrieval-augmented generation (RAG) systems.
- Knowledge of data governance and ML compliance frameworks.
- Hands-on experience in AI model optimization for edge or real-time environments.
Why Join Us :
- Work on cutting-edge ML and AI innovations across multiple domains.
- Collaborate with an exceptional team of data scientists and engineers.
- Opportunity to shape the organizations AI and ML roadmap.
- Competitive compensation with strong growth opportunities.
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