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 DevOps 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