Senior Fullstack Data Scientist

O projekcie:
- Stable employment. On the market since 2008, 1500+ talents currently on board in 7 global sites.- “Office as an option” model. You can choose to work remotely or in the office.- Workation. Enjoy working from inspiring locations in line with our workation policy.- Great Place to Work® certified employer.- Flexibility regarding working hours and your preferred form of contract. - Comprehensive online onboarding program with a “Buddy” from day 1. - Cooperation with top-tier engineers and experts. - Unlimited access to the Udemy learning platform from day 1.- Certificate training programs. Lingarians earn 500+ technology certificates yearly.- Upskilling support. Capability development programs, Competency Centers, knowledge sharing sessions, community webinars, 110+ training opportunities yearly.- Grow as we grow as a company. 76% of our managers are internal promotions.- A diverse, inclusive, and values-driven community. - Autonomy to choose the way you work. We trust your ideas.- Create our community together. Refer your friends to receive bonuses.- Activities to support your well-being and health.- Plenty of opportunities to donate to charities and support the environment. - Modern office equipment. Purchased for you or available to borrow, depending on your location.

Wymagania:
· 6+
years of experience in Data Science/AI engineering

· At
least 4+ years of experience in production-ready Python AI-related code
development.

· At
least 2+ years of experience in production-ready LLM-related code development,
preferably based on the Retrieval-Augmented Generation (RAG) concept.

· Strong
analytical and problem-solving skills with the ability to optimize AI solutions
for diverse applications.

· Strong
knowledge and experience in Generative AI, including LLMs, chatbots, AI agents,
and RAG mechanisms.

· Deep
understanding of LLM evaluators, validators, and guardrails.

· Hands‑on
experience with one or more GenAI frameworks: LangChain, LlamaIndex, LangGraph,
or similar orchestration stacks.

· Hands‑on
experience designing or operating MCP servers/clients for LLM agents

· Strong
Python skills, including production‑grade code, packaging, and testing for
data/ML services

· Solid
understanding of ML/AI concepts: types of algorithms, machine learning
frameworks, model efficiency metrics, model lifecycle, AI architectures.

· Proven
ability to collaborate effectively across technical and non-technical teams.

· Familiarity
with cloud environments such as Azure (preferred), GCP, or AWS, including
AI-related managed services.

· Familiarity
with CI/CD, testing, and containerized deployments.

· Excellent
communication skills in English, with the ability to convey complex technical
concepts to various audiences.



Nice-to-Have Skills & Knowledge:

· Experience
in designing and programming ML algorithms and data processing pipelines using
Python.

· Good
understanding of CI/CD and DevOps concepts, with experience working with
selected tools (preferably GitHub Actions, GitLab, or Azure DevOps).

· Experience
in productizing ML solutions using technologies like Spark/Databricks or
Docker/Kubernetes.

· Experience
with agentic AI development frameworks (e.g., BMAD, multi‑agent orchestration,
spec‑driven AI workflows).

Codzienne zadania:
- Lead discovery and solution design for GenAI use cases, translating business problems into concrete architectures (LLM decision, RAGs, fine tuning, agents, guardrails)
- Build end to end GenAI applications: data ingestion, retrieval layer, orchestration (e.g. LangChain/LlamaIndex/LangGraph), API/backend, and simple UI where needed.
- Design and implement RAG pipelines with vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks for relevance and robustness.
- Perform model selection, prompting strategies, and fine tuning (LoRA/QLoRA/SFT) for text, code, and multimodal models, including evaluation and A/B testing.
- Implement safety, compliance, and governance controls (input/output filters, PII handling, audit logs, human in the loop review where required).
- Collaborate with data engineers, product owners, and full stack developers on scalable architectures, SLAs, and integration with existing enterprise systems
- Gather technical requirements and estimate planned work.
- Mentor other data scientists/engineers in GenAI patterns, code quality, and best practices; contribute to internal libraries, templates, and reusable components.
- Stay current with GenAI landscape (new open and hosted models, agentic frameworks, evaluation techniques) and perform targeted PoCs to validate them.