Senior Gen AI Engineer
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What you'll do
- Design, fine-tune, and evaluate Generative AI models (LLMs and, where relevant, multimodal models) for enterprise use cases.
- Build and optimize prompting, RAG pipelines, agent workflows, and fine-tuning approaches.
- Work across open-source and commercial model ecosystems (e.g., OpenAI, Anthropic, LLaMA-family, Mistral, and others as appropriate).
- Implement guardrails, safety controls, and hallucination mitigation (grounding, verification patterns, retrieval tuning, structured outputs).
- Partner with consulting/product/business teams to identify, assess, and prioritize GenAI opportunities.
- Translate business problems into solution architectures, clear success metrics, and delivery plans.
- Evaluate feasibility, scalability, and expected ROI; communicate trade-offs and constraints.
- Produce solution blueprints, prototypes, and POCs that demonstrate value and can be industrialized.
- Design and manage data pipelines for training, fine-tuning, and inference.
- Build scalable, secure, cost-efficient GenAI services for production environments.
- Collaborate with engineering teams on deployment patterns, monitoring, and retraining strategies.
- Monitor model quality, latency, cost, drift, and reliability in production.
What they're looking for
- 5–9 years in software engineering, data science, ML engineering, or applied AI, including 2+ years focused on GenAI/LLMs.
- Strong hands-on experience with transformers, embeddings, vector databases, and retrieval patterns.
- Proficiency in Python and common GenAI tooling (e.g., LangChain, LlamaIndex, Haystack, Hugging Face, or equivalents).
- Experience with fine-tuning techniques (e.g., LoRA, PEFT, instruction tuning).
- Practical experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker/Kubernetes).
- Familiarity with MLOps practices, CI/CD, and model/prompt monitoring.
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
Nice to have
- Experience in consulting or enterprise product environments is a plus.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Senior GenAI Engineer (Poland/Hungary)
Company overview
Our client is a fast-growing business advisory and technology firm working at the intersection of Generative AI, data science, and digital transformation. They partner with global enterprise clients, with a strong concentration in life sciences, to deliver AI-driven solutions that improve commercial operations, accelerate insights generation, and enhance decision-making.
They have established delivery operations in multiple international locations, with a hub in Switzerland, and they are expanding their footprint with a new team in Poland and Hungary. As GenAI becomes a core enabler of enterprise transformation, we are hiring a Senior GenAI Engineer to design, fine-tune, and deploy Generative AI solutions in close collaboration with business stakeholders. This is a full-time, remote position based in Poland or Hungary.
Role overview
The Senior GenAI Engineer will build and operationalize Generative AI capabilities across internal platforms and client engagements. The role combines hands-on engineering (LLMs, RAG, agents, fine-tuning) with solution design and stakeholder engagement. You’ll translate real-world enterprise problems into scalable GenAI systems with measurable business impact.
Key responsibilities
1) GenAI model development & engineering
Design, fine-tune, and evaluate Generative AI models (LLMs and, where relevant, multimodal models) for enterprise use cases.
Build and optimize prompting, RAG pipelines, agent workflows, and fine-tuning approaches.
Work across open-source and commercial model ecosystems (e.g., OpenAI, Anthropic, LLaMA-family, Mistral, and others as appropriate).
Implement guardrails, safety controls, and hallucination mitigation (grounding, verification patterns, retrieval tuning, structured outputs).
2) Use case definition & business enablement
Partner with consulting/product/business teams to identify, assess, and prioritize GenAI opportunities.
Translate business problems into solution architectures, clear success metrics, and delivery plans.
Evaluate feasibility, scalability, and expected ROI; communicate trade-offs and constraints.
Produce solution blueprints, prototypes, and POCs that demonstrate value and can be industrialized.
3) Data, MLOps, & deployment
Design and manage data pipelines for training, fine-tuning, and inference.
Build scalable, secure, cost-efficient GenAI services for production environments.
Collaborate with engineering teams on deployment patterns, monitoring, and retraining strategies.
Monitor model quality, latency, cost, drift, and reliability in production.
4) Governance, risk, & responsible AI
Ensure responsible, ethical, and compliant use of GenAI in enterprise contexts.
Implement auditability, traceability, and explainability where required.
Address privacy, IP protection, and regulatory constraints (including GDPR where applicable).
Define and promote best practices, standards, and usage guidelines across teams.
Qualifications & experience
5–9 years in software engineering, data science, ML engineering, or applied AI, including 2+ years focused on GenAI/LLMs.
Strong hands-on experience with transformers, embeddings, vector databases, and retrieval patterns.
Proficiency in Python and common GenAI tooling (e.g., LangChain, LlamaIndex, Haystack, Hugging Face, or equivalents).
Experience with fine-tuning techniques (e.g., LoRA, PEFT, instruction tuning).
Practical experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker/Kubernetes).
Familiarity with MLOps practices, CI/CD, and model/prompt monitoring.
Experience in consulting or enterprise product environments is a plus.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
Key skills & attributes
GenAI depth: LLMs, RAG, agents, evaluation, and (where relevant) multimodal patterns.
Business orientation: ties solutions to measurable outcomes; avoids “demo-only” work.
Problem-solving: turns ambiguity into structured delivery and clear trade-offs.
Engineering mindset: production-grade systems, security, reliability, and cost control.
Communication: explains complex concepts to non-technical stakeholders plainly.
Ownership: end-to-end responsibility from idea → build → deploy → operate.
Adaptability: thrives in a fast-moving technical landscape.
Ethical awareness: strong focus on governance and responsible AI.
What they offer
Build cutting-edge GenAI solutions for global enterprise clients.
Exposure to high-impact, senior stakeholder problems and strategic AI initiatives.
Collaborative culture with high technical ownership and room to influence standards.
Growth paths into senior technical leadership (e.g., GenAI Lead / AI Architect).
Competitive compensation, performance incentives, and continuous learning support.
Company
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