Description

You will build production-ready AI solutions from concept through deployment.

Responsibilities

• Drive the full ML lifecycle, from data pipeline development and model training to production serving.
• Design and implement solutions using Large Language Models (LLMs) and build Agentic AI systems.
• Integrate core generative AI platforms, including Gemini and Amazon Bedrock, for scalable solutions.
• Implement MLOps best practices using tools like MLFlow for tracking and orchestration.
• Develop and execute testing strategies for LLM applications, utilizing frameworks for prompt evaluation.

Required Skills

• 4-7 years of professional experience in Machine Learning Engineering or AI Development.
• Master’s degree in Computer Science, Data Science, Engineering, or a quantitative field.
• Expertise in Python and ML libraries such as PyTorch, TensorFlow, and Scikit-learn.
• Proven experience deploying models on major cloud platforms (GCP, AWS, or Azure).
• Deep understanding of LLM architecture and fine-tuning processes.
• Practical experience with MLOps tools (e.g., MLFlow) and validation frameworks (e.g., DeepEval).
• Demonstrated ability to apply analytical skills to translate complex problems into engineering solutions.

Preferred Skills

• Hands-on experience developing applications using Google's Gemini API.
• Direct experience with AWS AI/ML services, specifically Amazon Bedrock.
• Experience optimizing models for latency and cost efficiency in production.

Key Skills

Education

Any Graduate