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14+ years of overall IT experience, including 5+ years in AI/ML and at least 2+ years in Generative AI or LLM-based solutions. Must have led AI delivery teams and deployed at least one GenAI or LLM solution into production. Role & responsibilities End-to-End GenAI Architecture: Architect complete Generative AI solutions integrating data ingestion, vectorization pipelines, model orchestration, retrieval-augmented generation (RAG), and deployment frameworks. Model Adaptation & Fine-Tuning: Guide teams on fine-tuning strategies (full, LoRA, QLoRA, PEFT) and dataset preparation. Oversee experimentation, model versioning, and evaluation pipelines to ensure domain alignment and governance. Data Pipeline & Engineering Oversight: Design and review data pipelines for text, structured, and multi-modal data. Ensure proper embedding generation, feature store management, and efficient retrieval layers that power LLM workflows. Agentic AI & Orchestration: Conceptualize and deliver multi-agent solutions using frameworks like OpenAI Agent SDK, Azure AI Agent Framework, LangGraph, or Oracle Agent Studio. Ensure modular orchestration and secure integration with APIs and business systems. LLMOps & Deployment: Oversee the integration of GenAI models into CI/CD pipelines, manage model monitoring, drift detection, decay analysis, and continuous improvement. Ensure readiness for production workloads. Cross-Hyperscaler Expertise: Evaluate and leverage AI capabilities from OpenAI (GPT models), Azure AI Studio, AWS Bedrock, GCP Vertex AI, and Oracle AI/Agent Studio. Select optimal cloud architecture balancing cost, flexibility, and compliance. Responsible AI & Governance: Define governance frameworks for data usage, prompt auditing, safety, and compliance. Establish reproducibility, version control, and ethical AI practices. Client & Stakeholder Engagement: Work directly with business leaders and clients to identify GenAI opportunities, translate needs into technical architectures, and drive delivery outcomes. Team Leadership & Mentorship: Lead multidisciplinary AI teams, mentor architects and developers, and promote continuous learning in GenAI frameworks, data engineering, and deployment practices. Preferred candidate profile Deep understanding of LLM architectures, fine-tuning, and parameter-productive adaptation methods Strong foundation in data pipelines, embedding generation, and vector databases (FAISS, Pinecone, Chroma, Milvus) Hands-on knowledge of RAG pipelines, prompt engineering, and context management Proficiency in LLMOps/MLOps practices CI/CD integration, monitoring, retraining, and model governance Awareness of cloud AI platforms and agent frameworks across multiple hyperscalers Understanding of Responsible AI principles including interpretability, bias, and safety Excellent leadership, program management, and client communication skills Preferred Background: Proven experience leading GenAI or LLM-based solution delivery in enterprise or consulting contexts .
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Most developer ads you'll see are written in English: 18,397 of the 24,795. That doesn't tell you whether the job itself requires you to speak English or work in English daily, so read each posting carefully. Don't assume the language of the ad matches the language of the team.
The employers posting the most developer roles are Link Group (369 jobs), Upvanta (257), jobgether (226), and OfferZen (212). If you're applying to any of these, research their hiring patterns. They move fast and post often, which means they're either scaling hard or replacing people who didn't fit.
In your interview, expect this: 'Walk me through the last time you had to debug something that took you more than an hour. What was it, what did you try first, and what would you do differently?' Hiring managers ask this to see if you think systematically or just try random fixes. Have a real example ready with specifics.