AI ML Engineer

About This Role

Join a dynamic team as a Mid-Level AI/ML Engineer, transforming novel concepts into robust, production-ready AI solutions. This role involves managing the full ML lifecycle, developing solutions with LLMs and Agentic AI systems, and integrating generative AI platforms.

Responsibilities

• Drive projects from initial ideation to production deployment, including data pipeline development, model training, validation, and serving.
• Design, implement, and optimize solutions utilizing Large Language Models (LLMs) and developing sophisticated Agentic AI systems to solve complex business problems.
• Leverage and integrate core generative AI platforms, including Gemini and Amazon Bedrock, to build scalable and efficient solutions.
• Implement MLOps best practices, utilizing tools like MLFlow for experiment tracking, model versioning, and pipeline orchestration.
• Develop and execute comprehensive testing strategies for LLM applications, including utilizing frameworks like DeepEval for prompt engineering and model output quality.
• Apply strong analytical skills to evaluate model performance, diagnose issues, and iterate on solutions to achieve maximum business impact.
• Work closely with cross-functional teams (data scientists, product managers, and software engineers) to define requirements and deliver integrated AI features.

Requirements

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

Qualifications

• Master's degree in Computer Science, Data Science, Engineering, or a quantitative field.
• 4-7 years of professional experience in Machine Learning Engineering, AI Development, or a closely related field.

Nice to Have

• Hands-on experience developing applications or services using Google's Gemini API or models.
• Direct experience with AWS services related to AI/ML, particularly Amazon Bedrock.
• Experience in building and managing multi-step, reasoning-based Agentic AI systems.
• Prior experience in optimizing models for latency and cost efficiency in a production environment.

Skills

* Required skills