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