Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development.

Arηs Group was acquired by Accenture in July 2024.

As a Senior Data & AI Engineer (m/f) at ARHS Group part of Accenture, you will lead the design, development, and deployment of advanced AI solutions across NLP, computer vision, and LLMs - scaling from edge devices to cloud infrastructure. You will shape intelligent systems that transform raw data into secure, high-performing AI-driven platforms, guiding technical strategy and mentoring engineering teams along the way.

Beyond project delivery, you will be the go-to expert and team lead for all Machine Learning matters, responsible for guiding others and consolidating organizational know-how around ML.

THE WORK:

• Act as the team lead for the ML domain: define engineering standards, ensure consistency and reusability across projects, and align AI practices with company goals.
• Architect and oversee AI/ML pipelines covering data ingestion, feature engineering, model training, deployment, and post-deployment monitoring
• Lead the development and customization of LLM-based solutions, including open-source models (e.g., Mistral, LLaMA) on hybrid cloud/on-prem infrastructure, ensuring privacy, latency, and performance constraints are met.
• Design and supervise the implementation of NLP pipelines for information extraction, classification, semantic search, and document understanding.
• Deliver production-grade AI services with high-availability APIs using FastAPI or equivalent frameworks.
• Drive deployment of AI workloads to both cloud platforms (Azure or AWS) and edge devices(e.g., NVIDIA Jetson Orin, Xavier NX) with real-time inference capabilities.
• Own infrastructure automation for reproducible and scalable ML environments using tools like Terraform, Ansible, Docker.
• Architect and integrate AI components into event-driven and serverless ecosystems (e.g., Azure Functions, Event Grid), ensuring observability, resilience, and scalability.
• Provide technical leadership and mentorship to junior/mid-level engineers, fostering a culture of best practices in MLOps, reproducibility, and ethical AI use.

Our roles require in-person time to encourage collaboration, learning, and relationship-building with clients, colleagues, and communities. As an employer, we will be as flexible as possible to support your specific work/life needs.

The work location for this role is an ARHS – Part of Accenture office.

HERE’S WHAT YOU’LL NEED:

• Minimum 5 years of hands-on experience in AI/ML engineering, with a demonstrated ability to lead and deliver complex AI initiatives across full-stack data pipelines and AI systems, and proficency in:
• Programming & APIs: Python, REST API architecture, performance tuning, secure API development.
• NLP & ML: Transformers, LLMs (Mistral, LLaMA, etc.), RAG architectures, prompt tuning, model compression, vector search, OCR pipelines, chatbot design.
• Cloud Platforms: Deep expertise in Azure and/or AWS (Functions, Glue, Logic Apps, AI Services).
• MLOps & Infrastructure: Docker, GitHub Actions, Infrastructure-as-Code (Terraform ,Ansible), CI/CD pipelines.
• AI System Integration: Edge inference (Jetson Orin, Xavier NX), Ollama/ONNX runtime, hybrid deployments, and workload orchestration.
• Interest or background in traditional software development (e.g., Java, .NET, TypeScript, Spring Boot) is a big plus.
• Any AWS/Azure certification is optional but valued.
• Strong communication in English or French (both is a plus).
• Demonstrated leadership in cross-functional teams, including mentoring and coaching.
• Autonomous and capable of strategic technical decision-making.
• Passion for building production-ready AI, with a bias for clean, documented, and maintainable solutions.
• Familiarity with Agile and DevOps cultures.
• Commitment to building and sharing internal knowledge, enabling collective team growth.