At Network Solutions, we’ve been trusted for decades to help people get online and stay ahead. We’ve been here since the beginning of the internet, and we’re still building for what comes next. As the original digital identity authority, we help secure domain names, protect brands, and safeguard the infrastructure businesses rely on. We empower our customers to own and manage the assets that define them online, while delivering enterprise-grade security to protect against virtual threats. Our team leverages modern, AI-accelerated tools to streamline how businesses manage their digital presence, making the most of our decades of experience. The Network Solutions team is here to help online businesses protect what’s theirs and build for tomorrow. That’s why millions trust us to protect their domains, brands, and websites every day.Senior AI Back-End Engineer What you’ll do & how you’ll make your mark  - Design & scale async REST/WebSocket APIs with Python 3.11+ + FastAPI, using dependency-injection, type hints, and clean vertical-slice architecture. - Implement multi-agent workflows with Semantic Kernel (handoff, sequential, concurrent) to route traffic among specialised LLM agents. - Integrate LLM providers (OpenAI GPT-4.1/mini, Google Gemini 2.5 Flash) behind a provider-agnostic layer for A/B and cost-aware routing. - Deliver Retrieval-Augmented Generation with vector stores such as Azure AI Search, pgvector, or Chroma. - Expose tool-using agents via OpenAI Assistants (Code-Interpreter) for data-analysis / file-manipulation tasks. - Evolve schemas with SQLModel / SQLAlchemy 2 & Alembic; tune Postgres for high-concurrency async access. - Maintain robust CI/CD (Bitbucket Jenkins) that lint, type-check, test, package (Docker), and deploy. - Instrument services with structlog JSON logs, OpenTelemetry traces, and cost/latency metrics; hold p95 < 100 ms. - Champion AI-assisted development (GitHub Copilot, Cursor) and share pragmatic problem-solving practices with the team. Who you are & what you’ll need to succeed.  - 5 + yrs building production APIs in Python; 2 + yrs with FastAPI (or similar async stack). - Deep knowledge of async I/O, Pydantic v2, DI, and observability. - Hands-on with Semantic Kernel or comparable agent frameworks. - Practical RAG implementations using Azure AI Search, pgvector, or Chroma. - Strong Postgres skills, including SQLModel/SQLAlchemy 2 and Alembic migrations. - Proven integrations or Side Projects with LLM APIs (OpenAI, Gemini) and structured-output design. - Dependency management via Poetry and virtual-env isolation. - End-to-end CI/CD ownership (build → scan → test → deploy). - Excellent analytical and problem-solving ability. - Remote work readiness with daily overlap of at least 09:00 – 13:00 EST. Nice to have - Event/message queues (RabbitMQ, Azure Service Bus, Kafka). - Observability stacks (Grafana, LangFuse) for LLM cost governance. Join us to build the agent-powered backbone of our AI platform—robust, model-agnostic, and ready for millions of users. Why you’ll love us.  - Work-life balance. Our work is thrilling and meaningful, but we know balance is key to living well.  - We celebrate one another’s differences.  We’re proud of our culture of diversity and inclusion. We foster a culture of belonging. Our company and customers benefit when employees bring their authentic selves to work. We have programs that bring us together on important issues and provide learning and development opportunities for all employees.  We have 20 + affinity groups where you can network and connect with Newfolders globally.  - We care about you. We are a family, and we care about you and your family’s physical and mental health by providing competitive HMO benefits – 175k MBL with one free dependent upon one year of service! We also give out Punctuality Bonus, Generous Vacation policy, and much more!  - Where can we take you? We’re fans of helping our employees learn different aspects of the business, be challenged with new tasks, be mentored, and grow their careers. Unfold new possibilities with #teamnewfold!  The target compensation range for this position is $130,000 to $150,000 annually. Individual salaries are determined by various factors including, but not limited to: candidate’s qualifications, such as skills, education, and experience, as well as internal equity and market conditions. #NetworkSolutions Employment with Newfold Digital is at-will and nothing in this Job Description should be interpreted or construed to alter the at-will employment relationship. This Job Description includes the essential job functions required to perform the job described above, as well as additional duties and responsibilities. This Job Description is not an exhaustive list of all functions that the employee performing this job may be required to perform. The Company reserves the right to revise the Job Description at any time, and to require the employee to perform functions in addition to those listed above.

• Own the development of new AI-powered features in our products as a hands-on engineer • Contribute to the company AI’s strategy, collaborating with engineers, product managers, designers, and data scientists • Develop evaluation methods for assessing the quality of AI features to ensure safety and clinical quality • Embrace a culture of continuous improvement within the engineering team

• Develop and deploy Agentic AI systems using frameworks like LangChain, CrewAI, AutoGen, etc. • Build applications using Large Language Models (OpenAI, Claude, Llama, etc.) • Design and implement Predictive AI / Machine Learning models • Develop AI chatbots, automation tools, and AI assistants • Work on RAG (Retrieval Augmented Generation) systems • Integrate AI solutions with web applications, CRMs, and enterprise systems • Fine-tune and optimize LLM models • Work with structured and unstructured data • Deploy AI models on cloud platforms (AWS, Azure, GCP) • Collaborate with business and product teams to identify AI use cases

• Build and maintain ML pipelines for training, testing, and deployment • Deploy machine learning and AI models into production environments • Manage model lifecycle (training, deployment, monitoring, retraining) • Automate workflows using CI/CD for ML models • Monitor model performance, drift, and data quality • Work with data scientists and AI developers to productionize models • Manage model versioning, data versioning, and experiment tracking • Deploy models on cloud platforms (AWS, Azure, GCP) • Containerize applications using Docker and Kubernetes • Implement monitoring and logging for ML systems • Ensure scalability, security, and reliability of AI systems