Software Engineer (Machine Learning)

Job Description

Company Description:

Factors.ai helps B2B marketers and prospecting teams to run targeted and optimal campaigns across the full buyer journey. We have AI agents for decisioning, orchestration and optimization built on top an unified data platform to achieve the same . Our goal is to enable B2B companies to drive more revenue and more efficiently and close deals faster. We are a startup with Product Market Fit with 300+ customers and fast growing.

Role Description:

We are looking for full time Software Engineer with experience in Machine Learning Engineering, who will be responsible for building, deploying, and maintaining scalable ML-powered APIs, data pipelines, and production-ready ML models. You will collaborate closely with Data scientists, Product Managers and Frontend Engineers.

Key Responsibilities:

API Development & Integration

Design, develop, and deploy scalable RESTful APIs for ML models.
Implement authentication, security, and access control for API endpoints.

Machine Learning Model Deployment

Containerize ML models using Docker and orchestrate deployments using Kubernetes, AWS SageMaker, or Vertex AI.
Optimize model inference performance with batch processing, model caching, and quantization.
Develop CI/CD pipelines for automated ML model deployment and monitoring.

Data Pipelines & ETL

Design and implement scalable data pipelines for offline scoring, training and inference using tools like Apache Airflow, Spark, or Prefect.
Work with structured and unstructured data.
Ensure data integrity, feature consistency, and model versioning in production.
Performance Monitoring & Model Observability
Implement logging, drift detection, and alerting for model degradation.
Optimize model serving infrastructure for cost efficiency and reliability.

Qualifications:

3+ years of experience in software engineering, with a focus on ML deployment.
Strong programming skills in Python and one additional language (Golang, Java, or Scala).
Strong in Data Structures and Algorithms. Knowledge of data science is a plus.
Experience in developing and deploying scalable APIs (FastAPI, Flask, Django, or gRPC).
Preferable hands-on experience with ML model deployment frameworks (TensorFlow Serving, Triton Inference Server, ONNX, etc.).
Proficiency in cloud platforms (AWS, GCP, or Azure) for ML workloads.
Knowledge of SQL & NoSQL databases (PostgreSQL, BigQuery, Redis, etc.).
Experience with Docker, Kubernetes, and CI/CD for ML (GitHub Actions, ArgoCD, or Jenkins).