AIML Engineer

Position Overview

AI/ML Engineer

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 in

  Python

  (NumPy, Pandas, Matplotlib, SciPy) for ML development.
• Backend/API development with

  FastAPI

  ,

  Flask

  , or

  Django

  .
• Database handling with

  SQL and NoSQL

  (PostgreSQL, MongoDB, BigQuery).
• Familiarity with

  CI/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 with

  model 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 with

  serverless architectures

  to 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 clear

  technical documentation and architecture reports

  .