Senior Applied AI/ML Scientist (B2B)
S
Sprout SocialMedia intelligence
If you prefer to work remotely from another location within Poland, we will accommodate you as best as possible.Full-TimeSenior
Salary26600 - 33300 PLN per month net b2b
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Job Details
- Languages
- En C1
- Experience
- 6+ years of experience building and operating production software systems; 2+ years hands-on experience shipping LLM-powered features in real-world applications.
- Required Skills
- PythonArtificial IntelligenceDatadogLangChain
Requirements
- Have 6+ years of experience building and operating production software systems.
- Have 2+ years of hands-on experience shipping LLM-powered features in real-world applications.
- Bring strong backend engineering skills; Python is preferred.
- Have experience with large language model APIs, open-weight models, or transformer-based techniques.
- Have experience with embedding models, similarity search, or vector databases.
- Have experience with prompt engineering, structured outputs, LLM evaluation, or automated testing.
- Have experience with LLMOps practices such as monitoring, versioning, and observability.
- Have owned a production system end-to-end.
- Have experience making and defending architectural trade-offs.
- Have experience mentoring engineers or leading technical design reviews.
- Have worked closely with Product and UX on feature delivery.
Responsibilities
- Architect and deliver ambient and interactive production agentic workflows.
- Design LLM-powered content enrichments for customer reporting, alerts, and intelligence briefings.
- Lead technical direction for MCP-compatible services, function-calling patterns, and multi-step reasoning workflows.
- Choose between retrieval, agents, fine-tuning, smaller models, and classical NLP approaches.
- Define structured prompting standards, output schemas, and reusable engineering patterns.
- Set evaluation practices using offline benchmarks, online experimentation, regression detection, and human review.
- Build safety guardrails and observability for tracing, cost, latency, quality, and drift.
- Evolve embedding and semantic search infrastructure, including chunking, hybrid search, and re-ranking.
- Partner with Product and UX to deliver features and lead AI architecture discussions.
- Review code, mentor engineers, and evaluate emerging models and frameworks.
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