Description

You will develop and deploy AI/ML solutions across the full lifecycle.

Responsibilities

• Drive projects from ideation through production deployment, covering data pipelines, model training, and serving.
• Design and implement solutions using Large Language Models (LLMs) and develop Agentic AI systems.
• Implement MLOps best practices, tracking experiments and managing model versioning.
• Develop and execute testing strategies for LLM applications, focusing on prompt engineering and output quality.
• Collaborate with data scientists, product managers, and engineers to define requirements and deliver AI features.

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/Data Science libraries (PyTorch, TensorFlow, SciKit-learn).
• Proven experience deploying models on GCP, AWS, or Azure.
• Deep understanding of LLM architecture and fine-tuning.
• Practical experience with MLOps tools like MLFlow and validation frameworks like DeepEval.
• Demonstrated ability to solve complex, ambiguous problems with engineering solutions.
• Experience in Ai/ml and Machine Learning Engineering.

Preferred Skills

• Hands-on experience with Google's Gemini API or models.
• Direct experience with AWS Services, particularly Amazon Bedrock.
• Experience building multi-step, reasoning-based Agentic AI systems.

Key Skills

Education

Any Gradute