As an AI Engineer at you will design, build, and deploy production-grade AI and machine learning solutions that solve complex business problems for global clients. You’ll work closely with data scientists, product managers, and client stakeholders to translate analytical models into scalable, reliable systems.
Key Responsibilities
- Design, develop, and deploy end-to-end AI/ML solutions from prototype to production.
- Implement machine learning models (classification, regression, NLP, time-series, generative AI).
- Build scalable data pipelines and feature stores for training and inference.
- Develop APIs and microservices for real-time and batch inference.
- Optimize model performance, latency, reliability, and cost.
- Apply MLOps best practices: CI/CD, model versioning, monitoring, and retraining.
- Collaborate with client teams to understand business needs and translate them into technical solutions.
- Ensure AI solutions meet security, privacy, and compliance standards.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
- Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Experience with cloud platforms (AWS, Azure, or GCP).
- Solid understanding of data structures, algorithms, and software engineering principles.
- Experience deploying models using Docker, Kubernetes, or similar tools.
- Familiarity with SQL and large-scale data processing.
Preferred Qualifications
- Experience with NLP, LLMs, or Generative AI (RAG, prompt engineering).
- Knowledge of MLOps tools (MLflow, SageMaker, Vertex AI, Azure ML).
- Exposure to consulting or client-facing environments.
- Strong communication and problem-solving skills.
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