Machine Learning Engineer

Expected Notice Period: 15 Days
Shift: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type: Remote

(*Note: This is a requirement for one of Uplers' client - A fast-growing, VC-backed B2B SaaS platform revolutionizing financial planning and analysis for modern finance teams.)What do you need for this opportunity?Must have skills required:

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.

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