What you will work on

LLM Integration & Conversation Management: Design, integrate, and operate LLM-based capabilities for text generation, summarization, and analysis using external APIs. You design and manage prompt systems, implement conversation handling with context persistence, and support streaming responses and real-time interactions. You optimize token usage and API costs, and design robust fallback strategies for rate limits and API failures.

Vector Search & Retrieval Architecture: Design and maintain vector database collections and embedding pipelines for structured and unstructured content ingestion. You implement semantic search, similarity matching, and Retrieval-Augmented Generation (RAG) systems, continuously optimizing retrieval accuracy and performance. You manage embedding model selection, versioning, and updates across the system.

Data Pipelines & Processing: Build and operate data ingestion pipelines from multiple sources, handling both batch and real-time processing. You implement data transformation, normalization, validation, and cross-system synchronization. You design scoring and ranking logic while ensuring data quality, consistency, and reliability throughout the pipeline.

Recommendation Systems & Personalization: Develop and operate content recommendation systems with personalization based on user behavior. You implement relevance scoring models, feedback loops for continuous improvement, and experimentation frameworks such as A/B testing to evaluate and refine recommendation quality.

Optimization, Evaluation & Monitoring: Track and analyze AI system and model performance metrics, monitor and optimize operational costs, and implement evaluation pipelines for AI outputs. You build dashboards to monitor system health, establish quality benchmarks, and define alerts to ensure reliable, transparent, and measurable AI system performance in production.

Your core experiences

• 4+ years software engineering experience

• 2+ years LLM/AI API integration in production

• Vector database and embeddings implementation

• Data pipeline development experience

• Strong understanding of NLP concepts

• Production ML/AI system ownership

• TypeScript or Python proficiency

Additional strengths

• RAG system implementation at scale

• LangChain, LlamaIndex, or similar framework experience

• Recommendation system development

• Information retrieval or search background

• Cost optimization for AI systems

• Real-time ML serving experience

• Fine-tuning or model training experience

• Research paper implementation

Benefits

At BlackMountain, we believe that good work emerges where people are trusted, supported, and given room to grow. We aim to foster a culture of trust, responsibility, and shared excellence — where people can work with clarity, autonomy, and a sense of purpose.

We offer flexible working models that acknowledge different life situations. You can work from our office in Vienna or Munich. Flexible working hours and home office options allow you to structure your work in a way that supports focus, balance, and long-term sustainability.

To support your professional development, we invest in learning and growth. You will have access to professional training and mentoring by our experienced consultants, creating space for skill development and long-term perspective.

We value plural perspectives and understand them as drivers of innovation. Therefore, we aim to diversify our team not only in terms of professional expertise, but also across socio-demographic diversity dimensions. To support this goal, we actively encourage applications from individuals who are underrepresented or face disadvantage on the basis of gender, sexual identity, disability, religion, or origin. Where qualifications are equal, we give preference to candidates from these groups.

For your day-to-day work, we provide a company laptop. Full-time employment is based on 38.5 hours per week, with compensation according to the Austrian collective agreement and the possibility of overpayment depending on qualifications and experience.

How to apply

Show us who you are – in your own way. You can send a written application, a presentation, work samples, or anything else that tells us:

• what you care about,

• what skills you bring to the team,

• what you’d like to learn,

• and why BlackMountain feels like the right place for it.

• Include links or attachments if helpful (GitHub, motivational letter, portfolio, case study).

We value individuality more than form.