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:

%CF; core machine learning and deep learning systems

%CF; LLM-based applications and knowledge pipelines

%CF; retrieval and reasoning systems (RAG)

%CF; 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

%CF; Designing and developing machine learning and deep learning models

%CF; Building scalable data pipelines for training, evaluation, and inference

%CF; Help in developing and productionizing AI systems as APIs and services

%CF; Designing and implementing RAG pipelines for knowledge-driven applications

%CF; Working with LLM frameworks such as LangChain and LlamaIndex

%CF; Building embedding pipelines and integrating vector search systems

%CF; Optimizing model performance, latency, and scalability

%CF; Collaborating with backend teams to integrate AI systems into products

Tech Stack

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

%CF; Strong proficiency in Python and machine learning libraries

%CF; Solid understanding of machine learning and deep learning fundamentals

%CF; Experience building and deploying ML models in production environments

%CF; Experience with data preprocessing, feature engineering, and model evaluation

Systems & AI Engineering

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

%CF; Understanding of scalable ML architectures and data pipelines

%CF; Familiarity with handling large datasets and compute-intensive workloads

%CF; Experience integrating ML models into real-world applications

Modern AI Stack (Important)

%CF; Experience with LangChain, LlamaIndex, or similar LLM frameworks

%CF; Understanding of RAG (Retrieval-Augmented Generation) pipelines

%CF; Experience with embeddings, semantic search, and vector databases

%CF; Familiarity with prompt design and LLM-based application workflows

Nice to Have

%CF; Experience with generative models, graph-based models, or diffusion models

%CF; Exposure to life sciences, cheminformatics, or scientific data

%CF; Experience with Docker, Kubernetes, and deployment pipelines

%CF; Experience working on AI-first or data platform products