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Чётко описанная senior-позиция с современным стеком и практической работой с LLM, но без зарплатного диапазона и с полной занятостью в офисе оценить предложение сложнее.
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Текст:
Hirify AI/Оригинал
TL;DR
Senior AI/ML Data Scientist (AI/ML, LLMs): Building and validating machine learning and deep learning models, prototypes, and AI solutions for banking and financial services with an accent on rapid experimentation, model robustness, and business-focused technical evaluation. Focus on benchmarking emerging architectures, developing LLM applications with fine-tuning and RAG, and delivering reliable models through rigorous validation, bias checks, and optimization.
Location: Putrajaya, Malaysia; fully onsite. The listed location is MYS - Petaling Jaya.
Company
hirify.global provides technology services and develops AI and machine learning solutions for business use cases.
What you will do
Track developments in AI/ML, including LLMs, architectures, and emerging techniques, and assess their relevance to business use cases.
Evaluate AI solution feasibility, recommend technical approaches, and benchmark vendor and open-source solutions.
Build rapid proofs of concept in 2–3 days and deliver validated prototypes within 2–4 weeks.
Design and develop ML/DL models end-to-end, including algorithm selection, feature engineering, optimization, performance, robustness, and bias checks.
Use generative AI tools such as Copilot, ChatGPT, Claude, and Cursor to accelerate prototyping and development.
Collaborate with product, business, and engineering teams in an agile environment; mentor junior team members through code reviews and knowledge sharing.
Requirements
7+ years of experience in Data Science or Machine Learning.
Proven experience in banking or financial services.
Strong expertise in supervised and unsupervised machine learning, deep learning, NLP, and time series.
Hands-on Python experience with scikit-learn, TensorFlow, PyTorch, XGBoost, and LightGBM.
Strong understanding of LLMs and modern techniques, including fine-tuning, RAG, prompt engineering, and agents.
Strong foundation in statistics, exploratory data analysis, feature engineering, and model evaluation.
Experience with SQL and large-scale data processing.
Exposure to cloud ML platforms such as AWS, Azure, or GCP.
Familiarity with Git, Jupyter, and Databricks.
Fluent English is required in the English-speaking work environment.
Nice to have
Experience in computer vision, reinforcement learning, or graph ML.
Strong analytical thinking, problem-solving, communication, and stakeholder management skills.
Ability to simplify complex topics into clear recommendations and maintain a critical mindset toward model outputs and AI-generated code.
Pragmatic approach focused on speed and business impact; comfort working in ambiguity.
Proactive ownership and mentoring mindset.
Culture & Benefits
Agile, fast-paced innovation environment focused on rapid experimentation and delivery of AI solutions.
Close collaboration with business, product, and engineering teams.
In-person collaboration is prioritized, with flexibility intended to support wellbeing, productivity, individual work styles, and life circumstances.
Inclusive work environment.
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