Machine Learning Engineer - Remote

About The Company

Halvik Corp is a leading provider of comprehensive digital services to a diverse portfolio of government agencies, including 13 executive agencies and 15 independent agencies. As a highly successful woman-owned business, Halvik has established a strong presence in the federal sector through more than 50 prime contracts and a team of over 500 professionals. The company specializes in delivering innovative solutions across various domains such as Digital Services, Advanced Analytics, Artificial Intelligence and Machine Learning, Cyber Security, and Cutting-Edge Technology. Halvik is committed to driving technological advancement and operational excellence, supporting the US Government in achieving its mission objectives. Join Halvik to be part of a dynamic organization that values innovation, collaboration, and excellence in service delivery.

About The Role

The Machine Learning Engineer will play a pivotal role in developing, deploying, and maintaining advanced machine learning models and AI solutions tailored for government applications. This position involves collaborating closely with data scientists, subject matter experts, and engineering teams to design scalable ML pipelines, optimize large language models (LLMs), and implement AI agent systems. The role requires expertise in model development, operationalization, and integration within cloud-based infrastructures, primarily leveraging AWS services. The successful candidate will ensure the robustness, accuracy, and efficiency of AI models in production environments while adhering to best practices in MLOps. This position offers an exciting opportunity to work on cutting-edge AI initiatives that directly support government operations and strategic goals.

Qualifications

• Minimum of 5+ years of experience in ML Engineering or Applied Machine Learning
• Proficiency in Python programming and experience with ML libraries such as scikit-learn, XGBoost, PyTorch, and TensorFlow
• Hands-on experience with Databricks, MLflow, and PySpark for data engineering and ML pipeline development
• Strong understanding of the ML model lifecycle and MLOps practices
• Experience working with AWS services including S3, EC2, Lambda, SageMaker, and Step Functions
• Proven ability to productionize machine learning models and integrate them into business systems
• Solid knowledge of mathematics and statistics relevant to machine learning and AI
• Experience with various machine learning algorithms, including supervised, unsupervised, and deep learning models
• Background in software engineering principles and best practices
• Hands-on experience with model training frameworks such as TensorFlow, PyTorch, and Hugging Face
• Practical knowledge of MLOps tools and workflows, especially on AWS platforms
• Experience working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAGs), and AI agent architectures
• Excellent communication, collaboration, and team-oriented skills

Responsibilities

• Collaborate with data scientists and subject matter experts to develop and refine machine learning models using curated datasets
• Conduct experiments, prototypes, and proof-of-concepts to validate model performance and feasibility
• Create scalable, reusable training pipelines utilizing Databricks notebooks and MLflow
• Implement and optimize LLMs, RAGs, and AI agent systems for various government applications
• Operationalize models through robust CI/CD workflows, deploying using MLflow, SageMaker, or custom APIs
• Monitor deployed models for accuracy, data drift, latency, and overall performance; manage retraining schedules accordingly
• Work closely with Data Engineering teams to align ML pipelines with Medallion Architecture layers (Bronze, Silver, Gold)
• Engineer high-quality features and maintain training and inference pipelines for production environments
• Leverage AWS cloud services including S3, EC2, Lambda, and Step Functions for platform engineering and deployment
• Document ML artifacts, processes, and performance metrics to ensure transparency and reproducibility
• Participate in agile project ceremonies, providing feedback and updates to stakeholders
• Share knowledge and mentor junior team members to foster a collaborative learning environment

Benefits

• Health, dental, and vision insurance plans
• Retirement savings plans with company contributions
• Paid time off and holidays
• Opportunities for professional development and continuous learning
• Work in a dynamic environment focused on innovation and impact
• Flexible work arrangements where applicable