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Software Engineer: Data Science and Data Engineering
3 semanas atrás
WIT Software creates products and solutions for the Telecom industry and has a very strong background on Generative AI, IP Messaging, Voice and Video, Unified Communications, Telecom services, TV middleware and Mobile Money. The company has 24 years of existence and works with some of the most relevant Telecom companies from Europe, Japan, United States and Africa. WIT is developing an innovative software product that leverages emerging concepts from Generative -AI and promotes the creation of innovative digital services for the Telecom industry. This product has the potential for commercial distribution to hundreds of mobile operators and service providers, reaching millions of users across various geographies worldwide.
We are seeking a highly skilled Software Engineer with expertise in Data Science and Data Engineering to join our team. In this role, you will design and implement robust data pipelines, develop analytics solutions, and collaborate with cross -functional teams to drive data -driven decision -making across the organization. This is an excellent opportunity for those who are passionate about developing successful software for the global market.
Key Responsibilities
. Exploratory Data Analysis – ingest, clean, profile and visualise data, flagging trends and anomalies. . Present findings and dashboards to managers and clients. . Translate raw data into business insights, identify patterns, fraud signals and operational KPIs. .
Design, build, and maintain scalable data pipelines for processing large datasets. . Develop, deploy, and optimize machine learning models for predictive analytics and AI applications. . Ensure data quality, integrity, and compliance with security and privacy standards. . Work on cloud -based data platforms (e.g., AWS, GCP, Azure) and integrate diverse data sources. .
Explore and implement new technologies and best practices in data engineering and data science. ∙ Researching and developing AI and ML models and techniques. ∙ Designing scalable pipelines to integrate Generative AI models, including RAG architectures, into backend services. ∙ Fine -tuning and adapting models for specific use cases with domain -specific data. ∙ Implementing and integrating vector databases (e.g., Pinecone, Milvus) for RAG workflows. ∙ Providing code documentation and contributing to technical documents.
Requirements
. Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field. . Proven experience as a Software Engineer, Data Scientist, or Data Engineer. ∙ Experienced with software engineering best practices. ∙ In -depth understanding of computer science fundamentals, Internet technologies and protocols. . Solid grasp of supervised / unsupervised modelling, feature engineering and model validation. .
Proficient with data -vizualization tools (e.g., Jupyter, Plotly, Apache Superset). . Hands -on experience with data lake technologies (e.g., Snowflake, Amazon S3). . Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch). . Familiarity with cloud platforms and containerization (Docker, Kubernetes). .
Excellent problem -solving, communication, and teamwork skills. ∙ Fluent in English with strong communication skills. ∙ Ability to communicate complex topics in a clear and structured way. ∙ Capable of exploring and quickly use new frameworks and tools. ∙ Proactive, able to take initiative and operate independently. ∙ Strong problem -solving skills and attention to detail. Preferred Skills: . MLOps tooling (Airflow, Kubeflow, MLflow, SageMaker, Vertex AI). . Exposure to LLM -powered workflows: RAG pipelines, vector databases, prompt -engineering. ∙ Proficiency in implementing RAG architectures using tools like LangChain or Haystack. .
Real -time/stream processing (Kafka, Flink, Kinesis) and feature -store design. . Advanced Statistics (survival analysis, causal inference, A/B testing). . Familiarity with data -privacy frameworks (GDPR DPIA, ISO and PCI. ∙ Proficiency in working with ML/AI libraries and frameworks like TensorFlow, PyTorch, or similar.
Benefits
∙ Work on cutting -edge Generative AI and data -driven projects. ∙ Opportunity to shape the next generation of AI -powered systems. ∙ Collaborative, innovative team culture. ∙ Competitive salary and benefits package. ∙ Professional development in the fast -evolving field of Generative AI.
