Qureos Mentor

Prodapt Solutions

<div>Overview:
<ul><li>Monitor and evaluate telecom operational processes delivered by outsourced partners for quality and compliance.</li><li>
Conduct audits, analyze performance metrics, and identify gaps in service delivery.</li><li>
Collaborate with vendors and internal teams to implement corrective actions and process improvements.</li><li>
Ensure adherence to SLAs, regulatory standards, and customer experience benchmarks.</li><li>
Prepare quality reports and support continuous improvement initiatives across telecom operations.</li></ul>
Responsibilities:
<ul><li>Monitor and evaluate telecom operational processes delivered by outsourced partners for quality and compliance.</li><li>
Conduct audits, analyze performance metrics, and identify gaps in service delivery.</li><li>
Collaborate with vendors and internal teams to implement corrective actions and process improvements.</li><li>
Ensure adherence to SLAs, regulatory standards, and customer experience benchmarks.</li><li>
Prepare quality reports and support continuous improvement initiatives across telecom operations.</li></ul>
Requirements:
<ul><li>Monitor and evaluate telecom operational processes delivered by outsourced partners for quality and compliance.</li><li>
Conduct audits, analyze performance metrics, and identify gaps in service delivery.</li><li>
Collaborate with vendors and internal teams to implement corrective actions and process improvements.</li><li>
Ensure adherence to SLAs, regulatory standards, and customer experience benchmarks.</li><li>
Prepare quality reports and support continuous improvement initiatives across telecom operations.</li></ul></div>

Quality Analyst

Overview: 
GCP, Big Query, Python, Pyspark, Dataproc, Airflow, SQL
 
Responsibilities: 
GCP, Big Query, Python, Pyspark, Dataproc, Airflow, SQL
 
Requirements: 
GCP, Big Query, Python, Pyspark, Dataproc, Airflow, SQL

ML Data Scientist

<div>Overview:
<div>
We are seeking a highly skilled and experienced Java Web Developer to join our dynamic team. The ideal candidate will have 5&ndash;7 years of hands-on experience in developing and maintaining enterprise-level web applications using Java, JSP/Servlets, and AJAX frameworks. You will be responsible for full-stack development, from designing responsive UIs to implementing robust backend logic and managing database interactions</div>
Responsibilities:
<h3 class="jobSectionHeader">Key Responsibilities:</h3><ul><li>
Design, develop, and maintain web applications using Java (17+), JSP, and Servlets.</li><li>
Implement dynamic and responsive user interfaces using HTML, CSS, JavaScript, and jQuery.</li><li>
Integrate asynchronous data exchange using AJAX (jQuery AJAX, DWR, or AjaxTags) and JSON.</li><li>
Develop and optimize SQL queries for Oracle 21c XE or MySQL databases.</li><li>
Collaborate with cross-functional teams to define, design, and ship new features.</li><li>
Deploy and manage applications on Apache Tomcat.</li><li>
Use Eclipse or PL/SQL Developer for development and debugging.</li><li>
Ensure application security, performance, and scalability.</li><li>
Write clean, maintainable, and well-documented code following best practices.</li></ul>
Requirements:
<ul><li>Experience with Maven/Gradle for build automation.</li><li>
Knowledge of connection pooling and JDBC optimization.</li><li>
Familiarity with RESTful services is a plus.</li><li>
Exposure to version control systems like Git.</li></ul></div>

Lead Engineer

Overview: 
GCP, Big Query, Composer, Python, Pyspark, Dataproc, Airflow, SQL
 
Responsibilities: 
GCP, Big Query, Composer, Python, Pyspark, Dataproc, Airflow, SQL
 
Requirements: 
GCP, Big Query, Composer, Python, Pyspark, Dataproc, Airflow, SQL

GCP Data Engineer

Overview: 
Design and implement complex ETL/ELT pipelines using PySpark and Airflow for large-scale data processing on GCP.Lead data migration initiatives, including automating the movement of Teradata tables to BigQuery, ensuring data accuracy and consistency.
 
Develop robust frameworks to streamline batch and streaming data ingestion workflows, leveraging Kafka, Dataflow, and NiFi.
 
Collaborate with data scientists to build ML-ready data layers and support analytics solutions.
 
Conduct proof of concepts (POCs) and document performance benchmarking for data throughput and velocity, ensuring optimized data workflows.
 
Enhance CI/CD pipelines using Jenkins and GitLab for efficient deployment and monitoring of data solutions.
 
Collaborate in agile teams for product development and delivery.
 
Ability to work independently and design data integrations and data quality framework.
 
Responsibilities: 
Strong proficiency in Python and SQL for data engineering tasks.
 
Strong understanding and experience with distributed computing principles and frameworks like Hadoop, Apache Spark etc.
 
Advanced experience with GCP services, including BigQuery, Dataflow, Cloud Composer (Airflow), and Dataproc.
 
Expertise in data modeling, ETL/ELT pipeline development, and workflow orchestration using Airflow DAGs.
 
Hands-on experience with data migration from legacy systems (Teradata, Hive) to cloud platforms (BigQuery).
 
Familiarity with streaming data ingestion tools like Kafka and NiFi.
 
Strong problem-solving skills and experience with performance optimization in large-scale data environments.
 
Proficiency in CI/CD tools (Jenkins, GitLab) and version control systems (Git).
 
GCP Professional Data Engineer certification.
 
Requirements: 
Design and implement complex ETL/ELT pipelines using PySpark and Airflow for large-scale data processing on GCP.Lead data migration initiatives, including automating the movement of Teradata tables to BigQuery, ensuring data accuracy and consistency.
 
Develop robust frameworks to streamline batch and streaming data ingestion workflows, leveraging Kafka, Dataflow, and NiFi.
 
Collaborate with data scientists to build ML-ready data layers and support analytics solutions.
 
Conduct proof of concepts (POCs) and document performance benchmarking for data throughput and velocity, ensuring optimized data workflows.
 
Enhance CI/CD pipelines using Jenkins and GitLab for efficient deployment and monitoring of data solutions.
 
Collaborate in agile teams for product development and delivery.
 
Ability to work independently and design data integrations and data quality framework.Strong proficiency in Python and SQL for data engineering tasks.
 
Strong understanding and experience with distributed computing principles and frameworks like Hadoop, Apache Spark etc.
 
Advanced experience with GCP services, including BigQuery, Dataflow, Cloud Composer (Airflow), and Dataproc.
 
Expertise in data modeling, ETL/ELT pipeline development, and workflow orchestration using Airflow DAGs.
 
Hands-on experience with data migration from legacy systems (Teradata, Hive) to cloud platforms (BigQuery).
 
Familiarity with streaming data ingestion tools like Kafka and NiFi.
 
Strong problem-solving skills and experience with performance optimization in large-scale data environments.
 
Proficiency in CI/CD tools (Jenkins, GitLab) and version control systems (Git).
 
GCP Professional Data Engineer certification.

GCP data engineer

<div>Overview:
<div>
we are looking for a strong ML/NLP/GenAI skills and are eager to work in a collaborative environment with global teams to drive NLP/GenAI application in business problems. You work independently and are always willing to learn new technologies. You thrive in a dynamic environment and are able to interact with various stakeholders and cross functional teams to implement data science driven business solutions.</div>
Responsibilities:
<div>
Working in collaborative environment with global teams to drive AI solutioning of business problems</div><ul><li>Developing end to end analytical solutions, and articulating insight findings to leadership. Provide data-driven recommendations to business by clearly articulating complex modeling concepts through generation and delivery of presentations.</li><li>Analyzing and modeling both structured and unstructured data from a number of distributed client and publicly available sources.</li><li>Designing and building scalable advanced NLP/SLM/LLM models to meet the needs of given Business engagement.</li></ul>
Requirements:
<div>
Experience in Machine Learning, Deep Learning model development &amp; Deployment from scratch in Python.</div><ul><li>Strong Knowledge and hands-on experience on implementation of large-scale NLP Projects and Fine tuning &amp; Evaluation of LLMs for downstream tasks such as text generation, Classification, summarization, question answering, entity extraction etc.</li><li>Working knowledge of NLP frameworks and libraries like NLTK, Spacy, Transformers, Pytorch, Tensorflow, hugging face api&rsquo;s.</li><li>Working knowledge about various Machine Learning Algorithms such as Supervised, Unsupervised, Generative AI . Should know the various data preprocessing techniques and its impact on algorithm's accuracy, precision and recall.</li><li>Knowledge &amp; Implementation Experience of Deep Learning i.e Convolutional Neural Nets (CNN), Recursive Neural Nets (RNN) &amp; Long Short Term Memory (LSTM), Generative Adversarial Networks (GAN), Deep Reinforcement Learning.</li><li>Experience with RESTful, JSON API services.</li><li>Working knowledge on these: Word embeddings, TF-IDF, Tokenization, N-Grams, Stemmers, lemmatization, Part of speech tagging, entity resolution, ontology, lexicology, phonetics, intents, entities, and context.</li><li>Experience in analyzing Live Chat/call conversation with agents.</li><li>Knowledge of Python, Sql, PySpark, Scala and/or other languages and tools.</li></ul></div>

Data Scientist - AIOPS

Overview: 
Senior Engineer Data Scientist - AI & Machine Learning
 .
 
Responsibilities include: 
End-to-End Model Development: Lead the entire lifecycle of machine learning projects, from data acquisition and feature engineering to model training, validation, deployment, and monitoring in a cloud environment.
 Generative AI Innovation: Architect and implement advanced Generative AI solutions. This includes developing and fine-tuning Large Language Models (LLMs), building robust Retrieval-Augmented Generation (RAG) and Multi-Modal RAG pipelines to ground models in factual data.
 AI System Evaluation: Design and implement comprehensive evaluation frameworks to measure the performance, fairness, and safety of LLMs and other generative models, ensuring they meet Verizon's high standards for reliability and accuracy.
 Advanced Analytics & Insights: Perform deep-dive data analysis using advanced statistical methods to uncover actionable insights. Wrangle and query large, complex datasets from multiple sources using expert-level SQL.
 AI Agent Development: Research, conceptualize, and contribute to the development of autonomous and semi-autonomous AI agents that can perform complex tasks and streamline business processes.
 Cloud-Native AI: Leverage Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, GCS, Google Kubernetes Engine) to build scalable, production-grade AI/ML solutions.
 Communication & Storytelling: Clearly articulate and present complex technical concepts and model results to both technical and non-technical audiences, influencing business strategy with data-driven narratives.
 What we're looking for...
 You are a deeply curious and highly skilled data scientist with a proven track record of delivering impactful AI/ML solutions. You possess a strong foundation in machine learning theory and an insatiable appetite for the latest advancements in Generative AI. You are a hands-on builder who thrives in a collaborative, fast-paced environment and is comfortable tackling ambiguous problems.
 
You'll need to have: 
Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field.
 Six or more years of professional experience in data science, machine learning, or a related role.
 Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
 Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning.
 Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases.
 Experience with Multi-Modal RAG systems (handling text, images, and other data types).
 Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets.
 Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services.
 
Even better if you have: 
A Master’s or Ph.D. in a relevant quantitative field.
 Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex.
 Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens)
 Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems.
 Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow).
 GCP Professional Machine Learning Engineer certification.
 
Responsibilities: 
Senior Engineer Data Scientist - AI & Machine Learning
 .
 
Responsibilities include: 
End-to-End Model Development: Lead the entire lifecycle of machine learning projects, from data acquisition and feature engineering to model training, validation, deployment, and monitoring in a cloud environment.
 Generative AI Innovation: Architect and implement advanced Generative AI solutions. This includes developing and fine-tuning Large Language Models (LLMs), building robust Retrieval-Augmented Generation (RAG) and Multi-Modal RAG pipelines to ground models in factual data.
 AI System Evaluation: Design and implement comprehensive evaluation frameworks to measure the performance, fairness, and safety of LLMs and other generative models, ensuring they meet Verizon's high standards for reliability and accuracy.
 Advanced Analytics & Insights: Perform deep-dive data analysis using advanced statistical methods to uncover actionable insights. Wrangle and query large, complex datasets from multiple sources using expert-level SQL.
 AI Agent Development: Research, conceptualize, and contribute to the development of autonomous and semi-autonomous AI agents that can perform complex tasks and streamline business processes.
 Cloud-Native AI: Leverage Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, GCS, Google Kubernetes Engine) to build scalable, production-grade AI/ML solutions.
 Communication & Storytelling: Clearly articulate and present complex technical concepts and model results to both technical and non-technical audiences, influencing business strategy with data-driven narratives.
 What we're looking for...
 You are a deeply curious and highly skilled data scientist with a proven track record of delivering impactful AI/ML solutions. You possess a strong foundation in machine learning theory and an insatiable appetite for the latest advancements in Generative AI. You are a hands-on builder who thrives in a collaborative, fast-paced environment and is comfortable tackling ambiguous problems.
 
You'll need to have: 
Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field.
 Six or more years of professional experience in data science, machine learning, or a related role.
 Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
 Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning.
 Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases.
 Experience with Multi-Modal RAG systems (handling text, images, and other data types).
 Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets.
 Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services.
 
Even better if you have: 
A Master’s or Ph.D. in a relevant quantitative field.
 Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex.
 Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens)
 Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems.
 Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow).
 GCP Professional Machine Learning Engineer certification.
 
Requirements: 
Senior Engineer Data Scientist - AI & Machine Learning
 .
 
Responsibilities include: 
End-to-End Model Development: Lead the entire lifecycle of machine learning projects, from data acquisition and feature engineering to model training, validation, deployment, and monitoring in a cloud environment.
 Generative AI Innovation: Architect and implement advanced Generative AI solutions. This includes developing and fine-tuning Large Language Models (LLMs), building robust Retrieval-Augmented Generation (RAG) and Multi-Modal RAG pipelines to ground models in factual data.
 AI System Evaluation: Design and implement comprehensive evaluation frameworks to measure the performance, fairness, and safety of LLMs and other generative models, ensuring they meet Verizon's high standards for reliability and accuracy.
 Advanced Analytics & Insights: Perform deep-dive data analysis using advanced statistical methods to uncover actionable insights. Wrangle and query large, complex datasets from multiple sources using expert-level SQL.
 AI Agent Development: Research, conceptualize, and contribute to the development of autonomous and semi-autonomous AI agents that can perform complex tasks and streamline business processes.
 Cloud-Native AI: Leverage Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, GCS, Google Kubernetes Engine) to build scalable, production-grade AI/ML solutions.
 Communication & Storytelling: Clearly articulate and present complex technical concepts and model results to both technical and non-technical audiences, influencing business strategy with data-driven narratives.
 What we're looking for...
 You are a deeply curious and highly skilled data scientist with a proven track record of delivering impactful AI/ML solutions. You possess a strong foundation in machine learning theory and an insatiable appetite for the latest advancements in Generative AI. You are a hands-on builder who thrives in a collaborative, fast-paced environment and is comfortable tackling ambiguous problems.
 
You'll need to have: 
Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field.
 Six or more years of professional experience in data science, machine learning, or a related role.
 Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
 Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning.
 Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases.
 Experience with Multi-Modal RAG systems (handling text, images, and other data types).
 Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets.
 Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services.
 
Even better if you have: 
A Master’s or Ph.D. in a relevant quantitative field.
 Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex.
 Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens)
 Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems.
 Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow).
 GCP Professional Machine Learning Engineer certification.

Data Analytics / Scientist Lead SR

Join Prodapt Solutions and explore 7   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.