Software Gigs

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs. Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team. The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

As a Teamcenter Integration Architect, you will design and implement large-scale PLM integrations between Siemens Teamcenter and enterprise systems (ERP, MES, CAD/ECAD, data warehouses, supplier portals). You'll translate business requirements into scalable technical architectures, define interface specifications and integration patterns (real-time, batch, event-driven, API-based), and coordinate with cross-functional teams across vendors and geographies. The role requires 4–6 years of hands-on experience architecting Teamcenter integrations using frameworks like T4EA, T4S, T4O, Teamcenter Gateway, and web services (REST, SOAP, SOA), plus working knowledge of ERP (Oracle EBS, SAP) and CAD/ECAD integrations.

As Senior Manager of AI Governance Technology at Teradata, you will own the end-to-end technical architecture of the company's AI Governance Operating System, including intake pipelines, compliance monitoring, and agentic workflows. You'll lead the engineering of sophisticated automation using tools like n8n and Claude APIs, architect the AI tool registry, and design real-time monitoring systems that anticipate governance challenges. You'll serve as the internal expert on Claude Enterprise, Microsoft Copilot, and Teradata AI Studio integrations, setting technical standards and specifications that guide the broader engineering team. This is a technical leadership role requiring deep expertise in enterprise AI governance, system architecture, and the ability to make complex engineering decisions with significant autonomy.

Lead the architectural design of server-side, backend, and cloud platform capabilities supporting Dexcom's continuous glucose monitoring products. Collaborate across product, cybersecurity, firmware, data, and mobile teams to translate business and regulatory requirements into secure, scalable, and maintainable platform solutions. Establish architectural direction for distributed services, APIs, data pipelines, event-driven systems, and shared platform capabilities. Guide technology exploration, system integration, performance testing, and serve as an authority for platform architecture principles and modern backend development practices across the organization.

This role develops and integrates device platforms with cloud management systems for wireless edge and IoT devices across Tier-1 carrier networks. The engineer will build middleware, APIs, and services; implement device management protocols (TR-069, TR-369/USP, SNMP); perform provisioning, telemetry ingestion, and firmware-over-the-air updates; and conduct end-to-end integration testing in lab environments simulating real-world conditions. The position requires proficiency in C/C++, Go, or Python on embedded and Linux systems, strong protocol debugging skills (Wireshark, GDB), and hands-on experience with cellular networking standards, REST/gRPC APIs, and remote device management. Success demands close collaboration across distributed teams, carrier certification support, and the ability to troubleshoot complex hardware-software-network integration issues independently.

Design, develop, and deploy end-to-end generative AI solutions including GenAI agents, LLMs, and applications to solve complex business challenges. Build data pipelines, design APIs for AI accessibility, and collaborate with cross-functional teams (developers, data scientists, business) to implement enterprise GenAI solutions. Requires 4+ years of full-stack application development (Java, Python, JavaScript) and 3+ years with data structures, algorithms, and data stores. Stay current with AI research and communicate technical concepts to non-technical stakeholders.