Machine Learning Engineer
Actif.ai
- Washington DC
- Permanent
- Full-time
Company: Actifai
Location: Washington, DC (Hybrid or Remote)The RoleActifai is looking for an experienced and motivated Machine Learning Engineer (MLE) to join our growing team. Embedded within the Data Science team, the MLE will play a critical role in scaling our ML and AI infrastructure and to support model development, deployment, and continuous reinforcement learning.This is a high-impact position tasked with shaping automation workflows and making architectural decisions. Part of the role will be building agentic AI systems. These LLM-based agents will automate complex, multi-step workflows, from customizing customer’s plan recommendations to assisting sales and support teams, enabling more personalized, efficient, and scalable customer experiences.Responsibilities
- Build, Maintain, and Enhance Infrastructure & Systems including:
- The API we use for model inference, ensuring performance, reliability, and scalability.
- Cloud-based infrastructure for scalable and efficient model training.
- Robust ML pipelines for streamlined deployment and experimentation.
- Application & Integration
- Build software layers that integrate ML models with our core application and APIs.
- Collaborate with data scientists, software engineers, data engineers, and product teams to deliver end-to-end AI solutions.
- Product Development R&D
- Build the infrastructure to deploy the models developed by our Data Scientists for new end-to-end AI products
- Design and develop agentic AI systems powered by large language models to support broadband use cases.
- Build agentic workflows that automate tasks such as customer query resolution and sales assistance.
- Automation & Tooling
- Lead the automation of our end-to-end client model onboarding process, reducing manual effort across:
- Data ingestion and transformation
- Exploratory data analysis
- Model training, validation, and deployment
- Unit testing and post-deployment updates in response to client-specific or market-driven changes
- Build tools and internal frameworks to improve model reproducibility, monitoring, and lifecycle management.
- 5+ years of experience in Python
- Proficiency in building and consuming RESTful APIs (we use FastAPI withPydantic)
- Familiarity with the machine learning lifecycle and modern ML engineering practices
- Hands-on experience with Docker, Airflow, and Kubernetes for containerization and orchestration
- Experience working in cloud environments (Google Cloud Platform preferred)
- Proficiency in SQL (BigQuery and PostgreSQL are a plus)
- Bachelor’s degree in computer science, systems engineering, machine learning, data science, or a related technical field — or equivalent professional experience
- Experience with A/B testing infrastructure or experimentation platforms
- Background in MLOps, model monitoring, or validation
- Familiarity with deploying models like Gradient Boosted Decision Trees, generative models, or neural networks
- Past involvement in zero-to-one product development initiatives