AI Solutions Engineer
Indexed description
If you are looking for a dynamic, innovative, and technology-driven company, Inetum is the place for you! Come be part of Inetum!
Job Description
Our client is a leading international financial institution undergoing a significant digital transformation journey. The organization is investing in innovative technologies, modern architectures, and data-driven solutions to enhance operational efficiency, agility, and time-to-market. As part of this transformation, the company is expanding its Artificial Intelligence capabilities to accelerate business innovation and deliver high-value solutions across multiple functions.
Key Responsibilities
- Design, develop, and maintain Retrieval-Augmented Generation (RAG) solutions, including vector databases, embeddings, retrieval mechanisms, and LLM integration.
- Develop and deploy Generative AI solutions leveraging Large Language Models (LLMs) and APIs.
- Integrate AI capabilities into existing Robotic Process Automation (RPA) workflows to improve business process efficiency.
- Assess, evaluate, and recommend AI frameworks, platforms, and technologies.
- Build Proofs of Concept (PoCs) and lead the industrialization of successful initiatives into production environments.
- Ensure AI solutions are scalable, observable, secure, and compliant with data privacy, security, and regulatory requirements.
- Monitor, evaluate, and optimize model performance, latency, and operational costs.
- Collaborate with cross-functional teams to identify and implement AI-driven solutions that create business value.
- Mentor and support RPA developers transitioning into AI-related roles and technologies.
- Minimum of 5 years of experience in software development.
- Strong proficiency in Python and software engineering best practices.
- Proven experience developing data pipelines, APIs, and system integrations.
- Hands-on experience in Artificial Intelligence projects, including Generative AI, Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), or Machine Learning (ML).
- Solid understanding of prompt engineering techniques and LLM optimization strategies.
- Experience working with Agile methodologies and modern DevOps practices, including CI/CD, Git, Kubernetes, and related technologies.
- Strong communication skills and ability to work effectively within multicultural and hybrid teams.
- Professional proficiency in English (spoken and written).
- Experience training, fine-tuning, or adapting smaller language models.
- Knowledge of Agentic AI architectures and frameworks.
- Familiarity with cloud-based AI services and MLOps practices.
- Experience with vector databases such as Pinecone, Weaviate, Milvus, or similar technologies.
- Organized, detail-oriented, and process-driven mindset.
- Excellent communication skills, with the ability to clearly present technical concepts and project updates to both technical and non-technical stakeholders.
- Team-oriented professional with the ability to work effectively across diverse teams.
- Client-focused approach with a commitment to delivering high-quality solutions.
- High level of motivation, accountability, and professional integrity.
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