AI/ML Engineering (Onsite)

The AI/ML engineer role requires a blend of expertise in machine learning operations (MLOps), ML Engineering, Data Science, Large Language Models (LLMs), and software engineering principles.

Skills you'll need to bring:

• Experience building production-quality ML and AI systems. Experience in MLOps and real-time ML and LLM model deployment and evaluation. Experience with RAG frameworks and Agentic workflows valuable.
• Proven experience deploying and monitoring large language models (e.g., Llama, Mistral, etc.). Improve evaluation accuracy and relevancy using creative, cutting-edge techniques from both industry and new research
• Solid understanding of real-time data processing and monitoring tools for model drift and data validation. Knowledge of observability best practices specific to LLM outputs, including semantic similarity, compliance, and output quality.
• Strong programming skills in Python and familiarity with API-based model serving.
• Experience with LLM management and optimization platforms (e.g., LangChain, Hugging Face).
• Familiarity with data engineering pipelines for real-time input-output logging and analysis.

Qualifications:

• Experience working with common AI-related models, frameworks and toolsets like LLMs, Vector Databases, NLP, prompt engineering and agent architectures.
• Experience in building AI and ML solutions. 
• Strong software engineering skills for the rapid and accurate development of AI models and systems. 
• Prominent in programming language like Python. 
• Hands-on experience with technologies like Databricks, and Delta Tables. 
• Broad understanding of data engineering (SQL, NoSQL, Big Data), Agile, UX, Cloud, software architecture, and ModelOps/MLOps. 
• Experience in CI/CD and testing, with experience building container-based stand-alone applications using tools like GitHub, Jenkins, Docker and Kubernetes

Responsibilities:

• Participate in research and innovation of data science projects that have impact to our products and customers globally.
• Apply ML expertise to train models, validates the accuracy of the models, and deploys the models at scale to production.
• Apply best practices in MLOps, LLMOps, Data Science, and software engineering to ensure the delivery of clean, efficient, and reliable code.
• Aggregate huge amounts of data from disparate sources to discover patterns and features necessary to automate the analytical models.

About Company

Improva