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Tech Stack
Tools & technologiesAzureBigQueryCloudGoIoTKubernetesPythonRust
About the role
Key responsibilities & impact- Develop high-performance, production-ready Python APIs using FastAPI to serve as the primary interface for on-device model inference
- Design, build, and maintain enterprise AI/ML platform services on multi-cloud infrastructure including model deployment, serving, and experiment tracking.
- Build robust CI/CD stacks to automate the testing of inference logic and the deployment of API services to edge devices.
- Implement ML orchestration workflows using LangGraph, MLflow, and custom orchestration layers for multi-agent AI systems.
- Develop and integrate AI workloads using ML-Ops and tracing tools like LangSmith.
- Design and implement automated data processing pipelines within FastAPI to handle real-time sensor or image inputs for the model.
- Bridge the gap between research and deployment by converting code from experimental into modular, maintainable Python packages.
- Ability to integrate and run pre-built AI models on local hardware using standard industry runtimes.
- Skilled at building the software logic required to process data inputs and handle model outputs efficiently.
- Expert at developing Python-based services and automating their deployment to devices via standardized pipelines.
- Capable of monitoring and optimizing software to run reliably within strict memory and hardware limitations.
- Experience deploying containerized models from Azure to edge devices using Azure IoT Edge or managed online endpoints.
- Experience building pipelines to structure, clean, and store data for model training or real-time retrieval (RAG) on edge devices.
- Ability to convert experimental data processing logic from notebooks into production-ready Python modules.
- Own platform reliability for AI services serving multiple business units.
- Implement observability, monitoring, and alerting for ML pipelines and inference services.
- Drive cost optimization across data platform workloads, cloud compute, and storage infrastructure.
- Proficient in using Azure Machine Learning Studio to manage the full lifecycle of models, including registration, versioning, and monitoring.
Requirements
What you’ll need- 8 plus years of experience in software engineering, data engineering, or ML platform engineering.
- Strong proficiency in Python and at least one systems language (Python, Go, Rust, C++).
- Deep hands-on experience with cloud-native data platforms (Databricks, BigQuery, Azure Data Lake, Kubernetes).
- Production experience building and deploying ML/AI pipelines including model serving, feature engineering, and experiment tracking.
- Experience with LLM application frameworks such as LangChain, LangGraph, and Langsmith or equivalent agentic AI orchestration tools.
- Experience with edge AI deployment on NVIDIA Jetson or similar embedded GPU platforms.
- Experience with knowledge graphs, ontology engineering, or semantic web technologies.
- Bachelor's / Advanced degree in Computer Science, Artificial Intelligence, or related field.
Benefits
Comp & perks- Employer-subsidized Medical, Dental, Vision, and Life Insurance
- Short-Term and Long-Term Disability
- 401(k) match
- Flexible Spending Accounts
- Health Savings Accounts
- EAP and Educational Assistance
- Parental Leave
- Paid Time Off (for vacation, personal business, sick time, and parental leave)
- 12 Paid Holidays
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonFastAPIML-OpsCI/CDLangGraphMLflowAzure Machine Learning StudioNVIDIA Jetsondata processing pipelinesmodel serving
Soft Skills
problem-solvingcommunicationcollaborationadaptabilityorganizational skillsleadershipcost optimizationmonitoringobservabilityreliability
Certifications
Bachelor's degree in Computer ScienceAdvanced degree in Artificial Intelligence