Software Engineer (AI/ML)

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., LangChain, Semantic Kernel, AutoGen, 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 SageMaker
• 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
• Bachelors or masters 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