AI / ML Engineer, Software Engineering About the Role
Experienced and forward - thinking AI/ML Engineer with a strong engineering background and experience leading technical teams. The ideal candidate thrives at the intersection of AI/ML systems engineering, platform development, and intelligent agent design. This individual will play a key role in building and scaling AI capabilities across our platform, with a focus on production - grade systems rather than data science Responsibilities - Design, build, and optimize scalable AI/ML infrastructure and services powering intelligent features across our platform. - Develop AI agents capable of autonomous decision - making, task execution, and multi - step reasoning across internal and customer - facing applications. - Architect and implement modular agent frameworks by integrating tools, APIs, and memory systems for dynamic and context - aware behavior. - Collaborate with product, data, and infrastructure teams to embed AI capabilities into production systems. - Evaluate and integrate state - of - the - art AI tools and frameworks to accelerate development and deployment. - Partner with Data Science teams to operationalize models, ensuring a smooth transition from experimentation to production. - Optimize agent performance for latency, reliability, and safety in production environments. - Stay current with the latest research and tools in LLMs, multi - agent systems, and cognitive architectures. - Contribute to the development of internal libraries, best practices, and reusable components for agentic systems. Qualifications - 8 years of experience in software engineering, with at least 2 years focused on AI/ML systems - Proven experience in building and deploying ML models in production environments - Hands - on experience with AI agent frameworks e.g., Lang
Chain, Semantic Kernel, Auto
Gen, or custom - built systems - Strong understanding of the ML lifecycle, including data pipelines, model training, evaluation, deployment, and monitoring - Familiar with MLOps tools such as MLflow, Kubeflow, or Sage
Maker - Deep understanding of LLM orchestration, prompt engineering, tool use, and memory architectures - Familiar with various LLM inference engines such as vLLM or SGLang - Experience in integrating agents with APIs, databases, and external systems - Familiar with retrieval - augmented generation RAG , vector databases, and knowledge graphs - Experience deploying AI systems in cloud environments AWS, GCP, Azure and utilizing containerization tools Docker, Kubernetes - Demonstrated ability to lead projects or small teams, with excellent communication and collaboration skills - Bachelor s or master s degree in computer science, engineering, or a related field - Experience with LLMs, generative AI, or multi - agent systems in production is a plus - Background in distributed systems or real - time data processing is a plus - Experience in the finance industry is a plus
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