onsite
Machine Learning Engineer - SDLC AI
Machine Learning Engineer - SDLC AI
The Machine Learning Engineer - SDLC AI will drive research and innovation in applying ML, Deep Learning, and LLMs to the Software Development Lifecycle. This role involves developing advanced AI models to identify and resolve code issues, building LLM-powered features like RAG, and engineering data pipelines for large code-centric datasets. You will also collaborate with product teams to integrate prototypes into Sonar's products and communicate complex technical concepts.
About the role
About the Role
As a Machine Learning Engineer - SDLC AI, you will be at the forefront of applying cutting-edge ML, Deep Learning, and LLMs to the Software Development Lifecycle (SDLC) to enhance our products.
Responsibilities
- Spearhead Research & Innovation: Stay on the cutting edge of ML, Deep Learning, and LLMs, specifically their application to the Software Development Lifecycle (SDLC), and identify novel opportunities to enhance our products.
- Develop Advanced AI Models: Design, prototype, and validate novel ML models that identify and resolve complex bugs, vulnerabilities, and code smells, going beyond the capabilities of traditional static analysis.
- Build LLM-Powered Features: Develop and implement advanced LLM-based solutions, including Retrieval-Augmented Generation (RAG) for contextual code analysis, fine-tuning models on proprietary codebases, and exploring agentic systems for automated code remediation.
- Engineer Data Pipelines: Build and manage robust data pipelines to gather, process, and version massive code-centric datasets required for training and evaluating specialized models at scale.
- Translate Prototypes to Products: Collaborate closely with engineering and product teams to integrate successful ML prototypes into Sonar's cutting-edge products, ensuring they meet the needs of our global user base.
- Communicate and Evangelize: Clearly articulate and document complex technical concepts and research findings to both technical and non-technical stakeholders.
Skills
MLDeep LearningLlmsSdlcAi ModelsRetrieval Augmented GenerationRagData PipelinesMachine Learning