[Job-31573] AI Engineer Master

Role overview This is a senior technical role within a Data Science and AI practice, designing and delivering production-grade artificial intelligence solutions for enterprise clients and internal teams. The work spans architecture through operations, with emphasis on Generative AI, agentic systems, Retrieval-Augmented Generation, and LLMOps. The position suits an engineer who combines deep hands-on expertise with technical leadership and a focus on measurable business impact.

Responsibilities - Architect AI solutions balancing performance, scalability, security, governance, cost, and long-term sustainability. - Build AI-powered products and platforms that meet enterprise engineering standards and business objectives. - Design intelligent and multi-agent systems covering planning, reasoning, task orchestration, and tool usage. - Develop advanced RAG architectures using embeddings, vector databases, GraphRAG, hybrid search, reranking, and grounding. - Integrate AI capabilities with enterprise ecosystems such as ERPs, CRMs, data platforms, and transactional systems. - Evaluate large language models, small language models, and multimodal components, defining quality and evaluation metrics. - Implement observability and monitoring across performance, cost, latency, usage, and model behavior. - Establish LLMOps pipelines covering development, testing, versioning, deployment, and ongoing monitoring. - Act as a technical reference, shaping architectural decisions and promoting engineering best practices.

Requirements - Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, or a related field. - Advanced English proficiency for global collaboration. - Proven experience developing and deploying Generative AI solutions in production. - Background designing technical architectures for complex, business-critical enterprise systems. - Demonstrated technical leadership across multidisciplinary initiatives. - Advanced proficiency in Python. - Hands-on experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or comparable frameworks. - Familiarity with OpenAI, Azure OpenAI, Anthropic, Gemini, or similar AI platforms. - Deep knowledge of RAG architectures, embeddings, and semantic search. - Experience with vector databases such as Pinecone, Weaviate, Qdrant, or Milvus. - Experience building APIs and microservices on Azure, AWS, or GCP. - Knowledge of observability, monitoring, and reliability engineering practices.

Nice to have - Experience building AI platforms used across multiple clients or business units. - Production experience implementing multi-agent architectures. - Familiarity with fine-tuning, PEFT, LoRA, and other model customization techniques. - Knowledge of Knowledge Graphs and GraphRAG. - Experience with automated LLM evaluation frameworks. - Advanced MLOps and LLMOps knowledge. - Experience with Databricks or Snowflake. - Open-source contributions, technical community involvement, or AI-related publications. - Advanced certifications in AI, cloud architecture, or software engineering.

Benefits and work setup - Health and dental insurance, plus life insurance. - Meal and food allowance. - Childcare assistance and extended paternity leave. - Profit Sharing and Results Participation program. - Gym and wellness partnerships through major well-being platforms. - Continuous learning platform plus partnerships with online and language learning providers. - Discount club and dedicated physical and mental well-being resources. - Inclusive hiring with accommodations and accessibility support throughout the selection process.