Lead Data Scientist / ML Engineer

19.3k - 28.6k PLN19 320 - 28 560 PLN/ mies./ mies.B2BB2B (netto)

SeniorZdalnie·WarszawaFull-time·B2B

#479431·Dodano 2 dni temu·0

Źródło: nofluffjobs.com

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Tech Stack / Keywords

AIML algorithmsML librariesLLMs SQLNoSQLCI/CD Python GenAI AWSMLOpsDocker KubernetesAI Agents

Podsumowanie oferty

Lead the development of AI and Machine Learning solutions for client projects, including an airport operations optimization engine and an LLM-powered recommendation engine. The work includes designing and implementing solutions using Python, AWS, and LLMs.

Wymagania

5+ years of commercial experience designing and implementing scalable AI solutions, including Machine Learning, predictive modeling, optimization, NLP, computer vision, GenAI, LLMs, and deep learning.
Proficiency in developing ML algorithms from scratch through production deployment.
Strong Python programming skills, including clean code and OOP design, and extensive knowledge of ML libraries such as Scikit-Learn, PyTorch, and TensorFlow.
Proven expertise deploying solutions in cloud environments such as AWS or Azure.
Knowledge of applying LLMs, including AI agents, semantic search, prompt engineering, multimodal embeddings, and RAG.
High proficiency in English (C1).
Excellent communication skills and consulting experience involving direct client interaction.
Working experience with SQL and NoSQL databases, including MongoDB, Snowflake, and Databricks.
Good knowledge of CI/CD principles, GitHub, and GitHub Actions.
Familiarity with MLOps practices, Kubernetes, and Docker.
Master’s or Ph.D. in Computer Science, Data Science, Mathematics, Physics, or a related field.

Nice to have:

Experience with Big Data technologies such as Spark, Hadoop, and Kafka.
Leadership and mentoring experience.

Obowiązki

Lead the development, implementation, and validation of Machine Learning solutions aligned with project objectives.
Lead the design of AI solution architectures for complex business goals.
Research, test, and select tools and technologies to meet business requirements.
Translate clients’ business problems into data science problems.
Architect and oversee data pipelines and workflows, setting standards for data quality and security.
Coordinate with Data Engineering and Software Engineering teams on AI applications.
Present findings and recommendations to stakeholders.
Prioritize and control tasks within an Agile/Scrum framework.

Benefity

Flexible work arrangements, including remote work or working from offices and coworking spaces.
Career paths and knowledge-sharing initiatives.
Language classes.
Sponsored training and conferences, including training materials and certifications through partnerships with Databricks and Anthropic.
Team-building events and an integration budget.
Medical and sports packages, eye care, and well-being support services, including psychotherapy and coaching.
Full work equipment, including a laptop and other necessary devices.
Dedicated onboarding buddy.
Private healthcare and a Multisport card.
Referral bonus and MyBenefit cafeteria.
Paid leave, training budget, and in-house trainings.
Free coffee.

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