AI/ML Engineer

About the Role:

• Client is seeking a highly skilled and motivated AI/ML Engineer to join client's team and drive the development and optimization of AI solutions.
• This role is ideal for someone who thrives at the intersection of machine learning, large language models (LLMs), and cloud infrastructure.
• Candidates will collaborate closely with business stakeholders to design, build, and refine intelligent systems that leverage cutting-edge technologies.

Key Responsibilities:

• Collaborate with business teams to understand requirements and translate them into ML models and prompt-based solutions.
• Design, develop, and fine-tune machine learning models, particularly those involving LLMs and generative AI.
• Optimize and adapt prompt engineering strategies to improve model performance and relevance.
• Integrate and deploy models using AWS services including Bedrock, S3, ECS, EC2, Lambda and other AI/ML related services.
• Build and maintain scalable data pipelines and APIs to support ML workflows.
• Monitor model performance and iterate based on feedback and metrics.
• Stay current with advancements in AI/ML and cloud technologies to ensure client's solutions remain cutting-edge.

Required Qualifications:

• Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field.
• 3 plus years of experience in machine learning, data science, or AI engineering.
• Hands-on experience with LLMs (e.g., OpenAI, Anthropic, Cohere) and prompt engineering.
• Strong proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Deep experience with AWS services, especially Bedrock, S3, EC2, and Lambda
• Familiarity with MLOps practices and tools for model deployment and monitoring.
• Excellent problem-solving skills and ability to communicate technical concepts to non-technical stakeholders.
• Strong programming skills in data analytics related languages and libraries, such as Python, R, Pandas, or JavaScript.
• Experience with AWS SageMaker for model development and model deployment.
• Understanding of quantitative/statistical/ML/AI modeling methodologies.
• Experience in ML engineering, including hands-on experience with Generative AI/LLMs.
• Experience with developing and deploying AI Agents for business problems.

Preferred Qualifications:

• Experience with fine-tuning or customizing foundation models.
• Knowledge of data privacy and security best practices in cloud environments.
• Familiarity with containerization (Docker) and container orchestration is a plus.