engineering
Added 2 weeks agoSenior Software Engineer - Applied AI/ML
Chicago, United StatesRemote
Responsibilities
- Promote best practices in software architecture, coding standards, and quality assurance while developing interfaces and workflow orchestration for real-time and batched solutions.
- Support evaluation and analysis of our solutions and models (latency benchmarks, noisy audio and data, accuracy, etc) Build and warehouse datasets for training and validation purposes, working closely with data scientists and other engineers.
- Ensure security, compliance, and compartmentalization of data for public safety and enterprise customers.
- Design and implement scalable ML/AI systems and pipeline Optimize model performance and accuracy to meet system requirements
Requirements
- Department Overview The AI Services team creates solutions by applying our
- experience in workflow development, system integrations, and infrastructure deployment.
- Our products leverage cutting edge machine learning capabilities to enable our customers to focus on providing faster responses, safer outcomes, and greater transparency.
- Job Description The AI Services team is looking for an experienced software engineer experienced with audio data, machine learning, and designing scalable systems.
- experience working with Python, SQL Must be a US Citizen with ability to obtain necessary security clearance as required by government contract Legal authorization to work in the U.S. indefinitely is required.
- Expert knowledge of software design and best practices for full development life cycle, including coding standards, code reviews, source control management, build processes, testing methodology, security, and audit/monitoring requirements.
- Highly proficient database and data modeling skills
- Experience developing containerized solutions (Docker, Kubernetes) and serverless infrastructure (e.g. AWS Lambda).
- Experience working with cloud-based solutions and configuration: Azure, AWS, and GCP.
- Familiarity with MLOps and full ML lifecycle management from prototype to production
- Experience working with ML models, including traditional networks and large language models (LLMs) Delivered solutions across domains like audio, NLP, generative AI, edge AI
- Experience with ML frameworks (PyTorch, Tensorflow) and infrastructure (e.g., Databricks, MLFlow, AWS SageMaker, Hugging Face)