Senior Software Development Engineer, Cloud and Devops

Build AI-Driven Systems:

•Architect and implement advanced AI systems, including multi-component pipelines, retrieval-augmented generation (RAG), and custom AI agents with multi-step reasoning.

•Integrate AI models into production software through robust APIs and scalable data pipelines.

•Adapt AI models and techniques to specialized domains, tailoring solutions for expert systems in areas such as legal, tax, and compliance.

Innovate:

•Evaluate and prototype cutting-edge AI techniques to solve business challenges

•Conduct proof-of-concept projects for new AI-driven features

•Stay current with AI research and emerging technologies

Provide Technical Leadership:

•Break down functional requirements into scalable technical specifications

•Mentor junior engineers and facilitate technical discussions

•Contribute to MLOps and LLMOps practices, both in design and implementation

•Act as a thought leader, sharing expertise in company-wide forums and representing the organization in emerging technology areas

Ensure Quality & Operations:

•Implement comprehensive testing frameworks and monitoring systems for AI model performance

•Ensure compliance with ethical AI principles and security standards

•Conduct systems analysis and recommend operational improvements

Collaborate Across Functions:

•Work closely with AI researchers, engineers, designers and product teams to translate AI capabilities into real-world applications.

•Optimize AI system performance considering factors like latency and resource usage

•Support application feature enhancements through AI capabilities

About You:

You are a fit for the position of Senior Software Engineer- AI if your background includes:

Required Skills and experience:

•Bachelor's degree in computer science or equivalent experience

•5+ years of experience in software engineering; at least 2 years focused on AI/ML

•Proficiency in Python and experience with AI/ML frameworks (e.g., PyTorch, TensorFlow)

•Strong understanding of machine learning principles, evaluation, and system design

•Knowledge of MLOps and the end-to-end lifecycle of AI-powered software applications

•Experience integrating AI models into production systems using APIs and data pipelines

Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes)