Machine Learning Engineer
Description
Formed in 2011, Inadev is focused on its founding principle to build innovative customer-centric solutions incredibly fast, secure, and at scale. We deliver world-class digital experiences to some of the largest federal agencies and commercial companies. Our technical expertise and innovations are comprised of codeless automation, identity intelligence, immersive technology, artificial intelligence/machine learning (AI/ML), virtualization, and digital transformation.
Position Description
We are seeking a talented Machine Learning Engineer to join our team and play a crucial role in designing, building, and deploying machine learning models. The ideal candidate will have strong Python programming skills, hands-on experience with AWS managed services for AI/ML, and a passion for solving real-world problems with machine learning solutions.
Responsibilites
Requirements
NON-TECHNICAL REQUIREMENTS:
Formed in 2011, Inadev is focused on its founding principle to build innovative customer-centric solutions incredibly fast, secure, and at scale. We deliver world-class digital experiences to some of the largest federal agencies and commercial companies. Our technical expertise and innovations are comprised of codeless automation, identity intelligence, immersive technology, artificial intelligence/machine learning (AI/ML), virtualization, and digital transformation.
Position Description
We are seeking a talented Machine Learning Engineer to join our team and play a crucial role in designing, building, and deploying machine learning models. The ideal candidate will have strong Python programming skills, hands-on experience with AWS managed services for AI/ML, and a passion for solving real-world problems with machine learning solutions.
Responsibilites
- Design, develop, and deploy scalable machine learning models and solutions.
- Collaborate with cross-functional teams to define ML problem statements, build prototypes, and implement solutions.
- Use AWS AI/ML services such as Amazon SageMaker, Bedrock, Textract, and other tools to build and optimize models.
- Develop and maintain robust ETL pipelines for pre-processing, cleaning, de-duplicating, finding similarities, and analyzing large datasets.
- Monitor, evaluate, and optimize model performance to meet business objectives.
- Automate model deployment and maintenance using MLOps best practices.
- Collaborate with DevOps and data engineering teams to ensure smooth integration of ML solutions.
- Stay updated with the latest trends and technologies in AI/ML and cloud computing.
- Participate in Coding Challenges on an as needed basis.
- Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions
Requirements
NON-TECHNICAL REQUIREMENTS:
- Must be a U.S. Citizen.
- Must be willing to work on Eastern Standard Business hours.
- Candidates must currently reside in the continental United States.
- Must have the ability to pass a 7-year background check and obtain/maintain a U.S. Government Clearance.
- Must be willing to participate in coding challenges on an as needed basis.
- Strong communication and presentation skills.
- Must be able to prioritize and self-start.
- Must be adaptable/flexible as priorities shift.
- Must be enthusiastic and have passion for learning and constant improvement.
- Must be open to collaboration, feedback and client asks.
- Must enjoy working with a vibrant team of outgoing personalities.
- Bachelor’s Degree in Computer Science, Data Science, or a related discipline and 5+ years of experience in Machine Learning, Data Science, or a similar role/field.
- Strong proficiency in Python with experience in libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
- Experience with AWS services for AI/ML, including SageMaker, Lambda, Step Functions, DynamoDB, and Glue.
- Solid understanding of supervised, unsupervised, and reinforcement learning techniques.
- Knowledge of MLOps principles and tools like MLflow, Kubeflow, or AWS equivalents.
- Familiarity with data preprocessing, feature engineering, and model evaluation metrics.
- Experience with natural language processing (NLP) and AWS Comprehend.
- Proficiency in containerization tools like Docker and orchestration systems like Kubernetes.
- AWS Certification (e.g., AWS Certified Machine Learning – Specialty).
- Experience with Databricks
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