AI Engineer

Our AI Engineering team helps leading organizations leverage the latest advancements in Artificial Intelligence to transform business processes and create measurable value. Projects span the full AI lifecycle, from identifying business opportunities and building proofs of concept to deploying enterprise-grade AI solutions. Typical initiatives include Generative AI applications, intelligent document processing, AI assistants, recommendation systems, predictive analytics, and automation solutions. We are looking for professionals with strong software engineering and AI expertise who can bridge business needs with scalable technical solutions. 

Requirements

• Minimum 3 years of experience in AI Engineering, Machine Learning Engineering, or Software Engineering.
• Strong understanding of machine learning, Generative AI, and Large Language Models (LLMs).
• Hands-on experience building and deploying AI solutions using cloud platforms such as Azure AI Services, Azure Machine Learning, AWS SageMaker, Google Vertex AI, or similar.
• Experience with LLM frameworks and orchestration tools such as LangChain, Semantic Kernel, LlamaIndex, or equivalent.
• Strong programming skills in Python and experience with software development best practices.
• Experience building APIs, microservices, and AI-enabled applications.
• Familiarity with vector databases, Retrieval Augmented Generation (RAG), embeddings, and prompt engineering.
• Experience with MLOps practices, CI/CD pipelines, monitoring, and model deployment.
• Knowledge of Docker, Kubernetes, Git, and cloud-native architectures.
• Strong communication skills and ability to translate business requirements into technical solutions.

Responsibilities

• Design, develop, and deploy AI-powered applications and services across the full solution lifecycle.
• Build and optimize Generative AI solutions using Large Language Models and retrieval systems.
• Collaborate with business stakeholders to identify use cases and assess AI adoption opportunities.
• Develop scalable and production-ready AI architectures following security and governance standards.
• Implement MLOps practices, model monitoring, and performance optimization mechanisms.
• Integrate AI solutions with enterprise systems, APIs, and data platforms.
• Evaluate emerging AI technologies and recommend their practical application in business environments.
• Support client workshops, PoCs, MVPs, and production implementations.
• Analyze solution effectiveness and measure business impact, adoption metrics, and ROI.

Our offer

• Competitive salary package (salary range provided during later recruitment stages).
• Remote work flexibility.
• Comprehensive benefits package.
• Opportunities for professional development and advancement.
• Access to the latest technologies and tools.
• Support for continued learning and certification.
• Collaborative and knowledge-sharing work culture.