Ingénieur en logiciel et consultant principal II

Accountabilities: Develop enterprise software solutions: Design, build, test, deploy, and maintain secure, scalable backend services and APIs using modern frameworks such as Python FastAPI, Java Spring Boot, and Node.js. Implement Generative AI capabilities: Integrate and deploy AI models and services, including OpenAI APIs, Azure OpenAI, Hugging Face, and other open-source solutions, into production applications. Build advanced AI architectures: Develop solutions using LLMs, Retrieval-Augmented Generation (RAG), agentic AI frameworks, and intelligent orchestration tools to address complex business challenges. Optimize AI models and workflows: Fine-tune and improve LLM performance for specific use cases, enhance prompt engineering strategies, and work with embedding models and vector databases to improve retrieval and response quality. Design intelligent automation: Build AI-powered workflows and automation solutions that improve productivity, streamline processes, enhance user experiences, and increase operational efficiency. Integrate enterprise systems: Connect AI and machine learning services with existing applications, APIs, databases, and business systems while maintaining reliability and interoperability. Ensure application security and quality: Apply secure coding practices, web and API security principles, and appropriate governance controls to protect sensitive information and support regulatory compliance. Deploy and manage cloud applications: Build and operate cloud-native applications, preferably on Microsoft Azure, using relevant services for AI, data storage, search, and machine learning. Improve performance and scalability: Troubleshoot technical issues, optimize application performance, and enhance the reliability and scalability of software and AI solutions. Collaborate across teams: Work with product managers, architects, legal professionals, and other technical stakeholders to define requirements, evaluate design options, and deliver solutions aligned with business objectives. Contribute to engineering excellence: Write, review, and maintain high-quality code; produce technical documentation; participate in architecture discussions; and identify opportunities for continuous improvement. Participate in Agile delivery: Contribute to daily stand-ups, sprint planning, backlog refinement, retrospectives, and other development activities that support iterative delivery and team collaboration. Requirements Professional experience: At least 3–4 years of software development experience, with a strong foundation in Python and/or Java Spring Boot. The position specifies both a minimum of three years in the required skills and four or more years of overall experience. Generative AI expertise: Practical experience working with Generative AI APIs and/or open-source models, including the integration of LLMs into software applications. RAG and AI orchestration: Experience with LangChain, LLM orchestration, or RAG pipelines, with familiarity in prompt engineering, embedding models, and vector databases. Backend and API development: Strong understanding of RESTful APIs, microservices architecture, asynchronous processing, and enterprise application integration. Programming skills: Proficiency in relevant programming languages and frameworks, including Python, Java, Spring Boot, and, where applicable, JavaScript or TypeScript. Cloud platforms: Experience deploying or supporting cloud-based applications, preferably using Microsoft Azure, including Azure OpenAI Service, Azure AI Search, Azure Machine Learning, and Azure Data Lake Storage (ADLS). Familiarity with AWS or Google Cloud Platform is also beneficial. Databases and messaging: Knowledge of relational and NoSQL databases, including SQL Server, MongoDB, and DocumentDB, as well as vector databases, Kafka, and caching technologies. DevOps and infrastructure: Familiarity with Docker, Kubernetes, Linux, continuous integration and continuous delivery (CI/CD) pipelines, and deployment automation. Security awareness: Understanding of web and API security frameworks, secure software development practices, and the importance of privacy, governance, and compliance in enterprise environments. AI/ML engineering: Familiarity with model deployment workflows, MLOps, AI pipeline orchestration, and emerging AI development frameworks. Collaboration and communication: Strong problem-solving, analytical, and communication skills, with the ability to explain technical concepts, work across disciplines, and manage multiple priorities effectively. Education: Relevant academic qualifications or equivalent practical experience in software engineering, computer science, artificial intelligence, or a related technical field. Preferred qualifications: Exposure to multimodal AI systems and agentic AI frameworks. Experience with Semantic Kernel, LangChain, and other LLM orchestration technologies. Familiarity with Azure OpenAI, Azure AI Search, Azure Machine Learning, and related cloud-native AI services. Experience with Docker, Kubernetes, AWS EKS, Jenkins, and automated CI/CD pipelines. Knowledge of frontend frameworks such as React or Angular. Experience with MLOps practices and production model deployment. Ability to contribute to full-stack development and enterprise software architecture. Benefits Competitive salary: Annual compensation ranging from $90,700 to $153,925, depending on experience, qualifications, and relevant expertise. Fully remote work: Work from home in the United States, subject to role requirements and applicable policies. Technology equipment: Company-provided laptop, monitors, headset, keyboard, and mouse to support an effective remote working environment. Internet reimbursement: Monthly connectivity reimbursement to help offset home internet expenses. Professional development: Opportunities to expand expertise in Generative AI, LLMs, cloud computing, enterprise software engineering, and intelligent automation. Career growth: Exposure to complex technical projects, architectural discussions, and innovative AI applications that support business transformation. Collaborative environment: Work with cross-functional teams across software engineering, product, architecture, legal operations, and technology. Meaningful impact: Contribute to solutions that improve operational efficiency, knowledge management, decision-making, and enterprise service delivery. Inclusive workplace: Join a team that values collaboration, accountability, integrity, continuous learning, and diverse perspectives. Additional employment conditions: Candidates must complete a background investigation. Employment-based visa sponsorship is generally not available for this position. Remote employees must maintain a dedicated, private workspace and reliable internet connectivity, with minimum speeds of 50 Mbps download and 5 Mbps upload.