
AIML Engineer
3 weeks ago
Position Overview
We are hiring an experiencedAI/ML Engineerto lead and shape our AI/ML initiatives. The ideal candidate will havehands-on experiencein machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions. This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems.
Key Responsibilities
- Lead and mentor a high-performing AI/ML team.
- Design and execute AI/ML strategies aligned with business goals.
- Collaborate with product and engineering teams to identify impactful AI opportunities.
- Build, train, fine-tune, and deploy ML models in production environments.
- Manage operations of LLMs and other AI models using modern cloud and MLOps tools.
- Implement scalable and automated ML pipelines (e.g., with Kubeflow or MLRun).
- Handle containerization and orchestration using Docker and Kubernetes.
- Optimize GPU/TPU resources for training and inference tasks.
- Develop efficient RAG pipelines with low latency and high retrieval accuracy.
- Automate CI/CD workflows for continuous integration and delivery of ML systems.
Key Skills & Expertise
1. Cloud Computing & Deployment
- Proficiency in AWS, Google Cloud, or Azure for scalable model deployment.
- Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML.
- Expertise in Docker and Kubernetes for containerized deployments
- Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation.
2. Machine Learning & Deep Learning
- Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.
- Experience with MLOps tools for integration, monitoring, and automation.
- Expertise in pre-trained models, transfer learning, and designing custom architectures.
3. Programming & Software Engineering
- Strong skills inPython(NumPy, Pandas, Matplotlib, SciPy) for ML development.
- Backend/API development withFastAPI,Flask, orDjango.
- Database handling withSQL and NoSQL(PostgreSQL, MongoDB, BigQuery).
- Familiarity withCI/CD pipelines(GitHub Actions, Jenkins).
4. Scalable AI Systems
- Proven ability to build AI-driven applications at scale.
- Handle large datasets, high-throughput requests, and real-time inference.
- Knowledge of distributed computing:Apache Spark, Dask, Ray.
5. Model Monitoring & Optimization
- Hands-on withmodel compression, quantization, and pruning.
- A/B testing and performance tracking in production.
- Knowledge of model retraining pipelines for continuous learning.
6. Resource Optimization
- Efficient use of compute resources:GPUs, TPUs, CPUs.
- Experience withserverless architecturesto reduce cost.
- Auto-scaling and load balancing for high-traffic systems.
7. Problem-Solving & Collaboration
- Translate complex ML models into user-friendly applications.
- Work effectively with data scientists, engineers, and product teams.
- Write cleartechnical documentation and architecture reports.
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