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
- 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
