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Python Engineer – (DevOps/Internal Tools) FULLLY REMOTE, ActivePrime, Inc.

Company:
Location: Remote
Published: 1970-01-01

REMOTE, REMOTE As a software engineer, you'll join a team tackling complex challenges in automating, streamlining, and scaling engineering and support workflows through the development of critical APIs and applications. This role requires actual engineering thinking to build robust internal systems. Ready to tackle complex engineering challenges that directly impact how our engineering and support teams work? Who we are NOT looking for: Agencies People who are looking for a side “hustle” or are starting their own business To apply: please fill out this form: https://forms.clickup.com/2257368/f/24wer-176357/RX6L92YGBHLB386KYI Additionally, please start your cover letter with: “I AM A PYTHON ENGINEER FOCUSED ON DEVOPS AND INTERNAL TOOLS” (in ALL CAPS) Required Skills: Core Engineering Strong Python experience Test-driven development (TDD) and pytest proficiency Familiarity with, and adherence to, PEP guidelines and Python coding standards Thoughtful development centered around performance, deeply optimized code, and proper code organization Bug identification and resolution with local testing Infrastructure & DevOps Experienced with Linux at the terminal level RESTful/CRUD API development and design best practices Designing and implementing resilient, internal-facing APIs and infrastructure tools Security practices and vulnerability prevention Git version control with branching strategies Databases PostgreSQL experience: schema design, optimization, advanced queries Professional Requirements English fluency (written/verbal) Previous experience working for a US-based company
Senior Data Engineer (100% Remote - USA Only), Six Feet Up

Company:
Location: Remote
Published: 1970-01-01

100% Remote, USA Six Feet Up is seeking a Senior Data Engineer with deep experience designing, building, and maintaining secure, scalable data systems. In this role, you will work with cross-functional teams to turn complex data challenges into reliable production solutions. You will design data pipelines, build processing workflows, improve data quality, support machine learning initiatives, and help clients make better use of structured, unstructured, time-series, sensor-based, and high-volume data. We are looking for someone who can bring technical leadership, sound engineering judgment, and strong communication skills to ambiguous data problems. The ideal candidate is comfortable working across data engineering, cloud infrastructure, machine learning support, and production software delivery. As a Senior Data Engineer, you will: Design, build, and maintain robust, scalable data pipelines Develop ETL/ELT workflows for collecting, transforming, validating, and storing data Work with cloud-based data processing and storage systems Implement data validation, quality checks, monitoring, and transformation workflows Process complex datasets, including noisy, high-volume, time-series, sensor, or device-generated data Support machine learning workflows, including data preparation, model training, evaluation, and production integration Collaborate with data scientists, researchers, software engineers, and client stakeholders Translate prototype data workflows into reliable, maintainable production systems Create systems that are well-documented, testable, secure, and ready for audit or review Communicate technical tradeoffs clearly to both technical and non-technical audiences If you are a motivated Senior Data Engineer seeking a challenging and meaningful role, we encourage you to apply. We are looking for someone with strong hands-on experience in: Data pipeline architecture and implementation ETL/ELT orchestration tools such as Airflow, Dagster, or similar platforms Python-based data engineering and data processing tools Cloud-based data infrastructure, storage, and processing Data modeling, schema design, validation, and quality-control practices Working with public, proprietary, structured, and unstructured datasets Time-series, sensor, IoT, or other high-volume data sources Supporting machine learning or data science teams with reliable data workflows Building reproducible, testable, and maintainable data systems Version control, automated testing, and collaborative software development practices Strong candidates may also have experience with: Machine learning pipelines for classification, prediction, recommendation, or categorization systems MLOps, model evaluation, experiment tracking, and reproducible ML workflows Processing noisy signal data or other data that requires cleaning, filtering, or feature extraction Healthcare, digital health, research, or regulated software environments HIPAA, privacy-preserving data architecture, or secure cloud data processing Working with sensitive, clinical, or user-generated data Helping researchers or domain experts scale early-stage algorithms into production-ready systems Designing systems that support thousands or more users A Plus: Experience with containers, CI/CD, DevOps practices, or Kubernetes Familiarity with AWS, Google Cloud, Azure, or similar cloud platforms Experience with consumer analytics, dashboards, or data visualization products Experience supporting FDA, SaMD, clinical validation, audit, or regulatory documentation efforts Experience designing secure data workflows for privacy-sensitive applications About You You are a senior engineer who brings structure to ambiguous technical challenges. You ask thoughtful questions, identify risks early, and know how to balance research needs, business goals, engineering quality, and long-term maintainability. You care about data quality, privacy, testing, documentation, and clear communication. You are comfortable designing architecture, writing production code, reviewing data workflows, and collaborating with people from different technical backgrounds. You enjoy helping clients move from ideas and prototypes to secure, scalable, production-ready systems.
Agentic AI Scientist, AstraZeneca

Company:
Location: Remote
Published: 1970-01-01

Durham, North Carolina, USA We are looking for Agentic AI Scientists eager to utilize their expertise in these advanced technologies to revolutionize our drug development processes. In the Pharmaceutical Technology and Development (PT&D) department, you will be a key player in transforming molecules into groundbreaking medical treatments. Your role involves contributing data science expertise into cross functional global pharmaceutical development projects in support of transforming the way we deliver medicines to patients. You'll play a pivotal role in shaping our AI strategy and driving the co-development of sophisticated HITL multi-agent systems. We are hiring two candidates for this position and the roles will be based at our dynamic site in Durham (USA). Essential skills/Experience: PhD in computer science, data science, artificial intelligence, machine learning or related fields. At least 3 years of experience in Deep Learning and ML Excellent coding skills in languages such as Python, R. Hands-on industrial experience designing multi-agent patterns, digital twins and experience with agentic AI design patterns, reinforcement learning. Extensive industrial experience with AI and ML frameworks like TensorFlow, PyTorch, Hands-on experience with GenAI orchestration frameworks such as LangGraph, CrewAI Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable Baselines. Hands-on industrial experience with applied machine learning domains such as deep learning, NLP, GenAI. Desirable skills/experience: Contributions to open-source projects. If you meet these criteria, please highlight merged GitHub PRs in your application. Strong publication record in the field of AI. Experience designing multi-agent systems in the pharmaceutical sector. Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry. Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI.
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