Applied AI Developer

Job Summary:

Join our customer’s team as an Applied AI Developer and work at the forefront of machine learning and generative AI. You will design, build, and deploy innovative AI solutions that leverage state-of-the-art technologies such as LLMs and advanced NLP, while collaborating closely with cross-functional teams. This is a unique opportunity to deliver impactful solutions on massive datasets within a high-caliber, remote-first environment.

Key Responsibilities:

• Build, refine, and use advanced ML engineering platforms and reusable components to deliver scalable AI solutions.
• Implement ML Ops processes, tracking model KPIs, monitoring drift, and establishing robust feedback loops for continuous improvement.
• Deploy and operationalize deep learning models, focusing on LLMs and generative AI, ensuring reliability and performance at scale.
• Design and orchestrate model pipelines including feature engineering, inferencing, and continuous training to meet strict SLAs.
• Collaborate with client-facing teams to understand high-level business context and translate it into technical requirements.
• Write production-ready code with a focus on testability, maintainability, and handling edge cases and errors gracefully.
• Participate actively in agile ceremonies, communicate progress effectively, and adhere to best practices for architecture, design, and code quality.

Required Skills and Qualifications:

• Bachelor’s or Master’s degree in Computer Science or a related field from a top-tier university.
• 4+ years of hands-on experience in machine learning, deep learning, and fine-tuning models (LLMs).
• Expert-level proficiency in Python; experience with backend API design and vector databases.
• Solid understanding of ML Ops, including measuring and tracking model performance, and MLFlow.
• Demonstrated experience in NLP, generative AI, and deploying real-time model predictions.
• Strong communication skills—both written and verbal—are essential for cross-functional collaboration.
• Experience with ML frameworks such as Keras and HuggingFace.

Preferred Qualifications:

• Familiarity with DevOps practices, CI/CD pipelines, cloud architecture, and data security.
• Experience in data engineering for big data systems and knowledge of PySpark or Scala.
• Background in computer vision and implementing end-to-end feature engineering pipelines.