AI​/ML Engineer

Why you:

As an AI / ML Engineer, you will be a contributor to the design, development, and implementation of Artificial Intelligence and Machine Learning solutions. Your primary focus will be on developing, implementing, and maintaining robust, scalable, and reliable AI/ML pipelines, with an emphasis on integrating and optimizing models, including Large Language Models (LLMs) and Generative AI technologies, into client and internal products.

Your role requires a deep knowledge of ML engineering best practices and the AI ecosystem, coordinating with data scientists and software engineering teams.

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What you will be doing:

• Develop, deploy, and maintain robust AI and machine learning pipelines for internal and client-driven projects.
• Deploy, manage, and scale AI models, including pre-trained models (e.g., LLMs) and custom ML models, into production environments.
• Work with data scientists to implement model prototypes into scalable, production-ready AI systems.
• Optimize and tune model performance, latency, and cost-efficiency on cloud platforms.
• Integrate AI/ML solutions with major cloud platforms (AWS, GCP, Azure) and utilize containerization technologies (Docker, Kubernetes) for consistent deployment.
• Apply standard MLOps practices, including continuous integration/continuous delivery (CI/CD), model versioning, monitoring, and maintenance systems.
• Coordinate with software engineering teams to ensure seamless integration of AI capabilities into applications and user workflows.
• Stay up to date with advancements in AI/ML technologies, Generative AI, and MLOps deployment strategies.

What we need from you:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related quantitative field.
• 4 to 5 years of progressive experience in machine learning engineering, software development with an ML/AI focus, or a related role.
• 1-3 years experience with ADK or other agentic frameworks.
• Strong programming skills in Python and proficiency in ML/AI frameworks such as Tensor Flow, PyTorch, or Scikit-learn.
• Hands-on experience in deploying and working with pre-trained models, such as Large Language Models (LLMs) or similar Generative AI technologies, into production environments.
• Solid experience with cloud platforms (AWS, GCP, Azure) and container orchestration using Docker and Kubernetes.
• In-depth knowledge of Data Engineering principles, ETL/ELT processes, and version control systems (e.g., Git).
• Experience in orchestrating Machine Learning pipelines using open-source tools like Kubeflow or managed cloud services.
• Experience in building and optimizing scalable AI/ML systems.
• Familiarity with MLOps best practices, including model monitoring, logging, and CI/CD pipelines for AI assets.
• Strong communication and teamwork skills, with an ability to work effectively across cross-functional teams including solution architects and data scientists.

What you will receive:

• Love your career :
  Competitive total rewards package. Blog during work hours; take a day off and volunteer for your favorite charity.
• Love your work/life balance :
  Flexibly work remotely from your home, there’s no daily travel requirement to an office! All you need is a stable internet connection.
• Love your coworkers :
  Collaborate with some of the best and brightest in the industry!
• Love your development :
  Hone your skills or learn new ones with our substantial training allowance; participate in professional development days, attend training, become certified, whatever you like!
• Love your workspace :
  We…