Software Engineer III – AI Engineering

Overview

In this AI/ML Software Engineer role, you will deliver AI/ML projects across the full development lifecycle using Agile methods to translate business needs into robust AI/ML solutions. You’ll create secure production code, maintain production-ready algorithms, and craft architecture artifacts for complex systems. You’ll work with large data visualizations and contribute to evolving software engineering communities, exploring new technologies. This position offers the chance to impact scalable financial systems and grow with cross-functional teams on high-impact AI initiatives.

Responsibilities

• Deliver AI/ML projects through the ML development life cycle using Agile methodology
• Translate business requirements into AI/ML specifications, define milestones, and ensure timely delivery
• Execute solutions, design, development, and technical troubleshooting with innovative approaches
• Create secure and high-quality production code, maintaining algorithms that run synchronously with systems
• Produce architecture and design artifacts for complex applications, ensuring design constraints are met
• Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets
• Identify hidden problems and patterns in data to drive improvements in coding hygiene and system architecture
• Contribute to software engineering communities of practice and events exploring new and emerging technologies

Key requirements

• Formal training or certification in software engineering concepts
• Hands-on experience developing production AI/ML solutions
• Familiarity with agentic workflows and frameworks such as LangChain, LangGraph, Auto-GPT
• Deep knowledge and experience in machine learning and artificial intelligence
• Expertise in at least one: LLMs, NLP, Knowledge Graph, CV, Speech Recognition, RL, Ranking/Recommendation, or Time Series Analysis
• Knowledge of data structures, algorithms, ML, data mining, information retrieval, and statistics
• Understanding of ML frameworks: TensorFlow, PyTorch, PyG, Keras, MXNet, Scikit-Learn
• Strong Python and Spark programming; proficiency with numpy, scipy; experience with distributed computation (multithreading, multi-GPU, Dask, Ray, Polars)
• Familiarity with AWS Cloud services (EMR, Sagemaker)
• Overall knowledge of the Software Development Life Cycle
• Solid understanding of Agile, CI/CD, application resiliency, and security

• collaboration with cross-functional teams
• problem-solving mindset
• proactive learning and curiosity
• LangChain
• LangGraph
• Auto-GPT

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