Software Engineer I - AI and ML

About the role

This role focuses on the full lifecycle of machine learning and Generative AI solutions, from initial design to deployment and monitoring. You will be responsible for creating models for tasks like classification, clustering, summarization, and information extraction, and then integrating them into production environments. This involves managing data pipelines, training models, and ensuring their performance once live.

A key part of the work involves collaborating with other teams to translate business needs into AI-driven features. This includes applying techniques such as natural language processing, outlier detection, and deep learning. You will also build robust, scalable Python-based RESTful APIs to make these ML models and AI services accessible.

Success in this position means not only delivering functional AI solutions but also optimizing database interactions for efficient data handling across both SQL and NoSQL systems. Staying current with new AI/ML advancements, such as RAG pipelines and LLM fine-tuning, and integrating them into existing products is also crucial for continuous improvement.

The listed salary range for this full-time remote position is $100,000 to $160,000 USD.

Skills that matter here

• Python: Strong proficiency in Python is required for building, scripting, and deploying AI/ML systems.
• PyTorch: Experience with deep learning frameworks like PyTorch is necessary for developing machine learning models.
• LLMs: Hands-on experience with Large Language Models is essential for building Generative AI features, including prompt engineering and RAG pipelines.
• REST: You will design and ship secure RESTful APIs to expose ML models as production-ready services.
• PostgreSQL: Proficiency in SQL databases like PostgreSQL is needed for managing structured data pipelines supporting AI workloads.
• Docker: Experience with Docker is beneficial for packaging and scaling AI services using containers.

Who this role suits

• A person who enjoys taking ownership of projects from conception to deployment.
• Someone who thrives in a collaborative environment, translating business needs into technical solutions.
• An individual who is proactive in learning and integrating new AI/ML advancements into their work.
• A developer who values building robust, scalable, and well-documented systems.

From the employer

WHAT YOU'LL DO:

• Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
• Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
• Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
• Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
• Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
• Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED:

• Python: Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
• NumPy · Pandas · FastAPI · Scikit-learn
• Machine Learning: Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
• PyTorch · TensorFlow · XGBoost · DBSCAN
• Generative AI (2+ yrs): Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
• LLMs · RAG · Prompt Eng. · Fine-tuning
• NLP & Search / Ranking: Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
• spaCy · BERT · FAISS · Elasticsearch
• API Development: Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
• REST · FastAPI · OAuth2 · Swagger
• Databases: Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
• PostgreSQL · MongoDB · Vector DBs
• GOOD TO HAVE: Cloud Platforms: Deploys and scales AI workloads on AWS, Azure, or GCP.
• AWS · Azure
• TypeScript / JavaScript: Frontend or full-stack exposure for building ML-powered product interfaces.
• TypeScript · React · Node.js
• MLOps: Manages the ML lifecycle — tracking, versioning, and pipeline automation.
• MLflow · Kubeflow · CI/CD
• Containerization & Orchestration: Packages and scales AI services using containers and cluster management.
• Docker · Kubernetes