Medvolt - Machine Learning Engineer

Role Overview

We are looking for a Machine Learning Developer to design and build scalable AI systems.

This role goes beyond traditional model development. You will work on:

core machine learning and deep learning systems

LLM-based applications and knowledge pipelines

retrieval and reasoning systems (RAG)

productionization of AI models and services

You will help translate complex data and scientific problems into robust, production-grade AI systems.

What You'll Work On

Designing and developing machine learning and deep learning models

Building scalable data pipelines for training, evaluation, and inference

Help in developing and productionizing AI systems as APIs and services

Designing and implementing RAG pipelines for knowledge-driven applications

Working with LLM frameworks such as LangChain and LlamaIndex

Building embedding pipelines and integrating vector search systems

Optimizing model performance, latency, and scalability

Collaborating with backend teams to integrate AI systems into products

Tech Stack

• Core ML: PyTorch, TensorFlow, Scikit-learn
• Data: NumPy, Pandas
• LLM / RAG: LangChain, LlamaIndex, vector databases, embeddings
• Backend Integration: FastAPI, Django (for model serving)
• Cloud: AWS (primary), Azure, GCP
• Other: REST APIs, async processing, Docker

What We're Looking For

Strong proficiency in Python and machine learning libraries

Solid understanding of machine learning and deep learning fundamentals

Experience building and deploying ML models in production environments

Experience with data preprocessing, feature engineering, and model evaluation

Systems & AI Engineering

Experience in productionizing ML systems (model APIs, pipelines, inference systems)

Understanding of scalable ML architectures and data pipelines

Familiarity with handling large datasets and compute-intensive workloads

Experience integrating ML models into real-world applications

Modern AI Stack (Important)

Experience with LangChain, LlamaIndex, or similar LLM frameworks

Understanding of RAG (Retrieval-Augmented Generation) pipelines

Experience with embeddings, semantic search, and vector databases

Familiarity with prompt design and LLM-based application workflows

Nice to Have

Experience with generative models, graph-based models, or diffusion models

Exposure to life sciences, cheminformatics, or scientific data

Experience with Docker, Kubernetes, and deployment pipelines

Experience working on AI-first or data platform products