Machine Learning Engineer; WFH

Position: Machine Learning Engineer (WFH)
Must have skills required :
Data Engineering, MLFlow, Supervised Learning, Time-Series Forecasting, Docker, Machine Learning, Python, SQL
Good to have skills :
async workflows, MLOps, Ray Tune, NLP

B2B SaaS platform (One of Uplers' Clients) is Looking for:
Machine Learning Engineer (Remote) who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you.

Role Overview Description
We are a fast-moving startup building AI-driven solutions to the financial planning workflow. Were looking for a versatile Machine Learning Engineer to join our team and take ownership of building, deploying, and scaling intelligent systems that power our core product.

Job Description
- Full-time Team:
Data & ML Engineering
Were looking for 5+ years of experience as a Machine Learning or Data Engineer (startup experience is a plus)

WHAT YOU WILL DO
- Build and optimize machine learning models from regression to time-series forecasting
Work with data pipelines and orchestrate training/inference jobs using Ray, Airflow, and Docker
Train, tune, and evaluate models using tools like Ray Tune, MLflow, and scikit-learn
Design and deploy LLM-powered features and workflows
Collaborate closely with product managers to turn ideas into experiments and production-ready solutions
Partner with Software and Dev Ops engineers to build robust ML pipelines and integrate them with the broader platform

BASIC SKILLS
Proven ability to work creatively and analytically in a problem-solving environment
Excellent communication (written and oral) and interpersonal skills
Strong understanding of supervised learning and time-series modeling
Experience deploying ML models and building automated training/inference pipelines
Ability to work cross-functionally in a collaborative and fast-paced environment
Comfortable wearing many hats and owning projects end-to-end
Write clean, tested, and scalable Python and SQL code
Leverage async workflows and cloud-native infrastructure (S3, Docker, etc.) for high-throughput data processing.

ADVANCED SKILLS
Familiarity with MLOps best practices
Prior experience with LLM-based features or production-level NLP

Experience with LLMs, vector stores, or prompt engineering
Contributions to open-source ML or data tools

TECH STACK

Languages:

Python, SQL
Frameworks & Tools: scikit-learn, Prophet, pyts, MLflow, Ray, Ray Tune, Jupyter
Infra:
Docker, Airflow, S3, asyncio, Pydantic