AI Architect – GenAI & Cloud
New
S
SiiAI consulting
Workplace type: remote; Residing in Poland requiredFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Fluent English, both written and spoken; Fluent Polish required
- Experience
- At least 10 years in software/data/ML engineering, with at least 3 years focused on AI/ML architecture at scale
- Required Skills
- PythonCloud ComputingMicrosoft AzureLangChain
Requirements
- At least 10 years of experience in software, data, or ML engineering.
- At least 3 years focused on AI/ML architecture at scale.
- Production-level fluency in at least one major cloud AI ecosystem: Azure, AWS, or GCP.
- Experience in client-facing roles, including leading technical workshops and presenting to C-level and engineering audiences.
- Ability to scope and budget AI projects, including cloud cost modeling, team sizing, and delivery planning.
- Strong hands-on Python skills.
- Practical experience with AI/ML frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent.
- Production experience building knowledge-grounded AI systems and agent architectures.
- Solid understanding of transformer architectures and their effects on context windows, latency, and cost.
- Fluent English, both written and spoken.
- Fluent Polish.
- Experience fine-tuning LLMs (LoRA, QLoRA, RLHF/DPO) is a nice-to-have.
- Consulting, system integration, or services delivery experience is a nice-to-have.
Responsibilities
- Lead client discovery sessions, technical workshops, and architecture presentations.
- Translate business problems into well-defined AI solution specifications.
- Analyze RFP/RFI documents, assess feasibility, and build technical proposals.
- Scope and estimate AI projects, including data readiness, infrastructure, cloud costs, and ongoing operating expenses.
- Design end-to-end architectures for generative and classical AI systems, including knowledge-grounded AI, agentic workflows, multi-model orchestration, and hybrid search.
- Select foundation models, vector databases, orchestration frameworks, and inference infrastructure based on project needs.
- Define MLOps/LLMOps practices and data pipelines, including model and prompt CI/CD, evaluation, chunking, embedding generation, and drift monitoring.
- Lead technical design reviews, produce architecture decision records, and mentor engineers.
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