What are the responsibilities and job description for the AI/ML Data Engineer position at Atika Tech?
Job Details
Position: AI/ML Data Engineer
Location: Irving, TX
Job Type: Fulltime
Job Summary:
We are seeking a highly skilled and experienced AI/ML Data Engineer to join our innovative team. The ideal candidate will be instrumental in designing, developing, and maintaining robust data pipelines and infrastructure that power our AI and Machine Learning initiatives, with a critical focus on implementing and managing Model Context Protocol (MCP). This role requires a strong blend of data engineering expertise, a deep understanding of AI/ML fundamentals, and proficiency in modern software development practices. You will work closely with data scientists, machine learning engineers, and product teams to translate complex data requirements into scalable, high-performance solutions, ensuring consistent and well-defined model contexts across the AI lifecycle.
Key Responsibilities:
Design, build, and maintain scalable and efficient data pipelines for AI/ML model training, evaluation, and deployment, integrating and enforcing Model Context Protocol (MCP) standards to ensure data consistency and model interpretability.
Define, implement, and manage the Model Context Protocol, establishing clear standards for how data, metadata, and environmental parameters are structured and presented to AI/ML models during training, validation, and inference.
Implement and optimize data storage solutions using relational and NoSQL databases such as PostgreSQL and MongoDB, considering how data contributes to and aligns with the defined Model Context Protocol.
Develop and manage data ingestion and streaming processes using message queuing systems like Apache Kafka, ensuring that all data flows adhere to and provide necessary information for the Model Context Protocol.
Apply advanced statistical and AI evaluation techniques, including regression, classification, information retrieval, power analysis, correlation, and statistical testing, utilizing and analyzing data within the established Model Context Protocol.
Develop and deploy machine learning models and AI-driven applications using containerization (Docker) and orchestration (Kubernetes), ensuring that model deployment and operational environments strictly conform to the Model Context Protocol.
Implement and maintain CI/CD pipelines for automated testing, deployment, and monitoring of AI/ML systems using tools such as Jenkins, Tekton, and Harness, with a focus on validating and enforcing Model Context Protocol adherence at every stage.
Ensure the reliability, scalability, and security of data infrastructure and AI/ML systems, particularly in maintaining the integrity and consistency of the Model Context Protocol across various environments.
Collaborate with cross-functional teams to understand data needs and deliver robust data solutions, advocating for and implementing best practices for Model Context Protocol definition and usage.
Stay abreast of the latest developments in AI/ML, data engineering, and data governance, especially concerning best practices for managing model context and reproducibility.
Required Skills and Qualifications:
Proficiency in AI Evaluation: Strong understanding and practical experience with AI/ML model evaluation techniques, including regression, classification, information retrieval, power analysis and correlation, and statistical testing.
Strong understanding of AI/ML fundamentals: Deep knowledge of Machine Learning, Deep Learning, Large Language Models (LLMs), and their practical implications and applications.
Model Context Protocol (MCP) Expertise:
Demonstrated experience in defining, implementing, and managing protocols for AI/ML model context, including data schemas, metadata management, and environmental configurations.
Ability to design and enforce standards for data consistency, versioning, and traceability across the AI/ML lifecycle.
Understanding of how to ensure model reproducibility and reliable inference through consistent contextual information.
Experience in integrating context management into data pipelines and MLOps workflows.
Strong Server-Side Engineering:
Proficiency in Python programming.
Experience with building and consuming REST APIs.
Familiarity with asynchronous and functional programming paradigms.
Database Expertise: Proficiency with relational and/or NoSQL databases, specifically PostgreSQL and MongoDB.
Message Queuing Systems: Experience with Apache Kafka for real-time data processing and messaging.
Containerization & Orchestration: Deep understanding and hands-on experience with Docker for containerization and Kubernetes for container orchestration.
CI/CD Familiarity: Familiarity with Continuous Integration/Continuous Deployment (CI/CD) tools and practices, including Jenkins, Tekton, and Harness, with an emphasis on incorporating context validation.
Tools and Technologies You Will Use
Programming & Libraries: Python, FastAPI, Pydantic, Pandas, Scikit-learn, NLTK
Databases: PostgreSQL, MongoDB
Messaging: Apache Kafka
DevOps: Docker, Kubernetes, Helm, Tekton, Harness
Development Environment: Mac or PC (your choice)
Productivity Tools: Access to time-saving AI tools such as GitHub Copilot and Cognition.ai's Devin.
Location: Irving, TX
Job Type: Fulltime
Job Summary:
We are seeking a highly skilled and experienced AI/ML Data Engineer to join our innovative team. The ideal candidate will be instrumental in designing, developing, and maintaining robust data pipelines and infrastructure that power our AI and Machine Learning initiatives, with a critical focus on implementing and managing Model Context Protocol (MCP). This role requires a strong blend of data engineering expertise, a deep understanding of AI/ML fundamentals, and proficiency in modern software development practices. You will work closely with data scientists, machine learning engineers, and product teams to translate complex data requirements into scalable, high-performance solutions, ensuring consistent and well-defined model contexts across the AI lifecycle.
Key Responsibilities:
Design, build, and maintain scalable and efficient data pipelines for AI/ML model training, evaluation, and deployment, integrating and enforcing Model Context Protocol (MCP) standards to ensure data consistency and model interpretability.
Define, implement, and manage the Model Context Protocol, establishing clear standards for how data, metadata, and environmental parameters are structured and presented to AI/ML models during training, validation, and inference.
Implement and optimize data storage solutions using relational and NoSQL databases such as PostgreSQL and MongoDB, considering how data contributes to and aligns with the defined Model Context Protocol.
Develop and manage data ingestion and streaming processes using message queuing systems like Apache Kafka, ensuring that all data flows adhere to and provide necessary information for the Model Context Protocol.
Apply advanced statistical and AI evaluation techniques, including regression, classification, information retrieval, power analysis, correlation, and statistical testing, utilizing and analyzing data within the established Model Context Protocol.
Develop and deploy machine learning models and AI-driven applications using containerization (Docker) and orchestration (Kubernetes), ensuring that model deployment and operational environments strictly conform to the Model Context Protocol.
Implement and maintain CI/CD pipelines for automated testing, deployment, and monitoring of AI/ML systems using tools such as Jenkins, Tekton, and Harness, with a focus on validating and enforcing Model Context Protocol adherence at every stage.
Ensure the reliability, scalability, and security of data infrastructure and AI/ML systems, particularly in maintaining the integrity and consistency of the Model Context Protocol across various environments.
Collaborate with cross-functional teams to understand data needs and deliver robust data solutions, advocating for and implementing best practices for Model Context Protocol definition and usage.
Stay abreast of the latest developments in AI/ML, data engineering, and data governance, especially concerning best practices for managing model context and reproducibility.
Required Skills and Qualifications:
Proficiency in AI Evaluation: Strong understanding and practical experience with AI/ML model evaluation techniques, including regression, classification, information retrieval, power analysis and correlation, and statistical testing.
Strong understanding of AI/ML fundamentals: Deep knowledge of Machine Learning, Deep Learning, Large Language Models (LLMs), and their practical implications and applications.
Model Context Protocol (MCP) Expertise:
Demonstrated experience in defining, implementing, and managing protocols for AI/ML model context, including data schemas, metadata management, and environmental configurations.
Ability to design and enforce standards for data consistency, versioning, and traceability across the AI/ML lifecycle.
Understanding of how to ensure model reproducibility and reliable inference through consistent contextual information.
Experience in integrating context management into data pipelines and MLOps workflows.
Strong Server-Side Engineering:
Proficiency in Python programming.
Experience with building and consuming REST APIs.
Familiarity with asynchronous and functional programming paradigms.
Database Expertise: Proficiency with relational and/or NoSQL databases, specifically PostgreSQL and MongoDB.
Message Queuing Systems: Experience with Apache Kafka for real-time data processing and messaging.
Containerization & Orchestration: Deep understanding and hands-on experience with Docker for containerization and Kubernetes for container orchestration.
CI/CD Familiarity: Familiarity with Continuous Integration/Continuous Deployment (CI/CD) tools and practices, including Jenkins, Tekton, and Harness, with an emphasis on incorporating context validation.
Tools and Technologies You Will Use
Programming & Libraries: Python, FastAPI, Pydantic, Pandas, Scikit-learn, NLTK
Databases: PostgreSQL, MongoDB
Messaging: Apache Kafka
DevOps: Docker, Kubernetes, Helm, Tekton, Harness
Development Environment: Mac or PC (your choice)
Productivity Tools: Access to time-saving AI tools such as GitHub Copilot and Cognition.ai's Devin.
Thanks & Regards,
Bhupender Singh
XL Impex Inc dba
Atika Technologies
5 Independence Way, Suite 300,
Princeton, NJ 08540
Direct:
LinkedIn URL:
Bhupender Singh
XL Impex Inc dba
Atika Technologies
5 Independence Way, Suite 300,
Princeton, NJ 08540
Direct:
LinkedIn URL:
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