AI Staff Software Engineer

Who We Are: Enhesa is the leading provider of regulatory and sustainability intelligence worldwide. As a trusted partner, we empower the global business community with the insight to act today and prepare for tomorrow to create a more sustainable future - positively impacting our environment, our health, our safety, and our future. Navigating the fast-changing compliance and sustainability landscapes, we help them understand not just what they should do (first) but also how to do it. Both in their unique business and anywhere in the world. Now and in the future. Our Mission: Identify EHS requirements for the industry Provide EHS compliance tools to companies Advise companies in developing and implementing corporate EHS strategies Enhesa’s core clients include Fortune 500 multinational companies. For more information, visit www.enhesa.com As part of our highly dynamic team, we offer: A competitive salary package & benefits with a flexible home-working policy Work/life balance and a fast-paced and driven environment Accountability and pride for your projects We’re looking for a Staff Software Engineer who is equal parts builder and architect—someone who can take a feature from napkin sketch to production deployment and has strong opinions (loosely held) about how modern software should be built. You’ll work across the full stack: designing APIs, standing up infrastructure, wiring data pipelines, and shipping microservices that power AI-driven products. This isn’t a role where you’ll be handed a spec and asked to implement it. You’ll be expected to challenge assumptions, propose better approaches, and move fast with the help of AI coding tools that are core to how we work. What you'll do: Design, build, and own microservices and APIs end-to-end—from data model to deployment pipeline to monitoring. Architect and maintain CI/CD pipelines, container orchestration, and infrastructure-as-code across cloud environments (Azure preferred, AWS/GCP a plus). Work hands-on with databases (relational and vector), message queues, and search infrastructure—configuring, tuning, and scaling them in production. Collaborate with the AI team to integrate ML models and LLM-powered features into production services. Champion engineering standards: code review culture, repository structure, testing strategies, and deployment practices. Leverage AI-assisted development tools (Copilot, Claude Code, Cursor, etc.) to accelerate delivery—and help the team do the same. Operate in Linux environments daily: scripting, debugging, managing services, and keeping systems healthy. What We’re Looking For The short version: a creative, resourceful engineer who builds real things, has seen what scales and what doesn’t, and gets genuinely excited about the new capabilities AI tooling unlocks. Languages: Strong Python skills plus meaningful experience in at least one compiled/systems-level language (C#/.NET, C++, Go, Rust, or similar). You know when a scripting language isn’t enough. Cloud & Infrastructure: Production experience with cloud platforms (Azure, AWS, or GCP). Comfortable with Terraform or Bicep, Kubernetes/AKS, and the general lifecycle of infrastructure-as-code. Data & Storage: Hands-on work with SQL databases, Elasticsearch, vector databases (Qdrant, Pinecone, etc.), Kafka or equivalent message brokers. You’ve set them up, not just queried them. Microservices & APIs: Deep experience designing, deploying, and maintaining distributed services. RESTful APIs, gRPC, event-driven patterns—you have opinions about when to use each. CI/CD & DevOps: You’ve built pipelines (Bitbucket Pipelines, GitHub Actions, Azure DevOps, etc.) and care about how repos are structured, how tests run, and how code gets to production safely. Linux: Daily-driver comfort. Shell scripting, process management, troubleshooting, system configuration. AI Development Tools: Active user of AI coding assistants. You’re not just curious—you’ve integrated them into your workflow and can articulate what they’re good (and bad) at. Bonus Points Experience with MLflow, experiment tracking, or ML pipeline tooling. Familiarity with Azure Kubernetes Service (AKS) and the Azure ecosystem specifically. Background in NLP, RAG architectures, or LLM integration patterns. Contributions to open-source projects or a visible portfolio of shipped work. Experience in regulated industries (EHS, legal, financial, healthcare). What You’ll Get A seat on a small, high-impact AI team building products that matter at global scale. Direct access to leadership—short feedback loops, real influence on architecture and direction. A culture that treats AI tools as force multipliers, not novelties. Competitive compensation, benefits, and flexibility. The chance to help shape the engineering culture of a company in a transformative growth phase. How We Work Our tech stack includes Azure/AKS, Terraform, Bitbucket Pipelines, Kafka, Elasticsearch, Qdrant, SQL, and a multi-provider LLM strategy spanning OpenAI, Anthropic, and Google. We build AI microservices in Python and deploy them as containerized services. We use MLflow for experiment tracking and Splunk for observability. We believe the best engineers are opinionated about their tools and pragmatic about their deadlines. If you have a strong point of view about how a codebase should be organized, how a deployment should work, or why a particular pattern is the right (or wrong) one—we want to hear it. If you are ready to join our journey, please apply! Equal Opportunity Employer Enhesa is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other legally protected characteristic.