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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # A/AI Machine Learning Engineering Stf - **Company:** Lockheed Martin - **Location:** Fort Worth, TX, United States - **Experience:** Expert - **Salary:** $150,800.0 - $280,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Cloud Database, Computer Programming, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Systems, Data Warehousing, Database Queries, Software Debugging, Distributed Systems, Github, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Operational Databases, Performance Tuning, Query Optimization, Tensorflow, DataOps, Azure Machine Learning, Azure Data Lake, SciPy, Software Engineering, SQL Databases, Sql Optimization, Pytorch, Snowflake, Apache Spark, Deep Learning, Gitlab, Git, Pandas, Containerization, Scikit Learn, Kubernetes, Information Technology, Optimization Algorithms, Data Management, Machine Learning Operations, Software Version Control, Data Pipelines, Docker, Amazon Redshift, Databricks - **Published:** September 18, 2026 - **Apply:** https://dejobs.org/x/x/31BFC756B0B54FD1888E85EC70D7D6B9/job/ ## About the Role * Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or related technical field * Master's degree or PhD in Machine Learning, Artificial Intelligence, or related field preferred * 10+ years professional experience, 5+ years in production ML * Expert Python, deep learning frameworks (TensorFlow/PyTorch) * ML fundamentals, cloud platforms, MLOps experience * Leadership and communication skills Desired Skills * Programming : Expert-level proficiency in Python; strong SQL skills; experience with ML libraries (scikit-learn, pandas, NumPy, SciPy) * Machine Learning Frameworks : Experience with TensorFlow, PyTorch, or JAX for deep learning; scikit-learn for traditional ML * ML Fundamentals : Deep understanding of machine learning algorithms (especially anomaly detection, time series, NLP), statistical modeling, and optimization techniques * Data Engineering Platforms : Hands-on experience with modern data orchestration tools (Airflow, Prefect, Dagster) and data transformation frameworks (dbt) * Big Data Technologies : Production experience with Spark, distributed computing, and processing large-scale datasets * Data Warehouses : Experience with cloud data warehouses (Snowflake, BigQuery, Redshift, or Databricks) * SQL Mastery : Advanced SQL skills including query optimization, window functions, CTEs, and performance tuning * Cloud Platforms : Production experience with AWS, Azure, or GCP (both ML services and data platform services like S3, GCS, Azure Data Lake) * Version Control & CI/CD : Proficiency with Git, GitHub/GitLab, and CI/CD pipelines for both ML models and data pipelines * MLOps : Experience with model deployment, containerization (Docker), orchestration (Kubernetes), and integrating ML into data platforms ## Description Responsible for developing, integrating, and deploying artificial intelligence and automation algorithms for data engineering systems, supporting the technology development life cycle from requirements generation through development, integration, and testing. Develops, integrates, and implements algorithms to enable intelligent data pipeline orchestration, automated data quality validation, metadata discovery, anomaly detection, and predictive data operations functionality within data engineering platforms and tooling. Translates requirements from data engineering teams and applies them to development code, integrating AI, machine learning, and automation algorithms into data platforms, ETL/ELT workflows, and data infrastructure. Determines optimal methods to acquire, process, and operationalize knowledge from data systems; implements algorithms to train systems to recognize data patterns, predict pipeline failures, optimize resource allocation, and automate routine data operations tasks. Responsible for various phases of developing and maintaining AI/automation software from requirements generation, software design and development through integration, testing, troubleshooting, debugging, and implementation. Reviews test outcomes, conducts troubleshooting, and debugs issues in production data systems. Develops human-machine interface scenarios for data engineers and analysts, breaking complex data workflows into automated tasks. Documents interface requirements and implements intuitive interfaces that enable data teams to leverage AI capabilities without deep ML expertise. ## Related Videos - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Vectorize all the things! 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