Senior Python Engineer with AI Exposure

Aether Biomedical; United States; Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Vacation days 26

Health insurance

Mental health support

Learning platforms access

Hybrid work option

Certification reimbursement

Job summary

Aether Biomedical is seeking a Senior Python Engineer to design, develop, and deploy AI and ML driven solutions at scale. You will build models, embeddings, and RAG pipelines, and productionize ML services across Azure, AWS, and GCP.

Join a team focused on enterprise-ready AI, data pipelines, and observability, with hybrid work options and strong learning opportunities through internal platforms.

Qualifications

• Strong proficiency in Python with ML libraries and frameworks.
• Hands-on experience with LLMs, embeddings, transformers, and RAG pipelines.
• Experience deploying ML models on AWS, Azure, or GCP.
• Experience with vector databases and related search services.
• Familiarity with Docker, Kubernetes, and CI/CD for ML.
• Background building scalable ML services and MLOps tooling.

Responsibilities

• Design, build, and deploy ML and generative AI models, incl. LLMs and RAG.
• Develop scalable AI services and microservices on cloud-native tech.
• Optimize models for performance, cost, and accuracy.
• Create data pipelines for training, validation, and inference.
• Collaborate with data engineering on data ingestion and governance.
• Implement CI/CD for ML models and MLOps workflows.
• Monitor drift and retraining needs; manage model lifecycle.
• Integrate AI systems with enterprise apps and cloud platforms.

Skills

Python

NumPy

Pandas

PyTorch

TensorFlow

Transformers

LLMs

MLOps

Docker

Kubernetes

Cloud platforms

Vector databases

Tools

LangChain

LangGraph

PyTorch/TensorFlow tooling

SageMaker

Databricks

FAISS

Pinecone

Weaviate

Job description

Position overview

We are seeking a skilled Senior Python Engineer to design, develop, and deploy AI and ML driven solutions that enhance business capabilities, automate processes, and improve customer and employee experiences. The ideal candidate has a strong foundation in machine learning, large language models (LLMs), data engineering, and cloud platforms, with the ability to productionize models at scale.

Responsibilities

• Design, build, and deploy machine learning and generative AI models, including LLMs, embeddings, transformers, and RAG pipelines.
• Develop scalable AI services and microservices using Python, REST APIs, and cloud native technologies.
• Optimize models for performance, accuracy, and cost efficiency.
• Work with structured and unstructured datasets for feature engineering, vectorization, and model training.
• Build data pipelines for training, validation, and inference.
• Collaborate with data engineering teams on data ingestion, storage, and governance.
• Implement CI/CD pipelines for machine learning models and MLOps workflows.
• Monitor model performance and drift, and implement retraining strategies.
• Manage model lifecycle processes, logging, and observability.
• Integrate AI systems with enterprise applications, APIs, and cloud platforms such as Azure, AWS, and GCP.
• Build Retrieval Augmented Generation (RAG) architectures leveraging vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search.
• Ensure solutions align with enterprise security, compliance, and responsible AI standards.
• Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions.
• Communicate AI capabilities and limitations to non technical stakeholders.
• Conduct proofs of concept (POCs), demonstrations, and conceptual solution design activities.

Requirements

• Strong proficiency in Python, including NumPy, Pandas, PyTorch, TensorFlow, and Transformers.
• Hands‑on experience with LLMs, including OpenAI, Azure OpenAI, Anthropic, and Llama models.
• Experience with AWS Bedrock/AgentCore, Google Vertex AI, Azure AI Foundry, or other Agentic AI platforms, as well as open-source Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or the OpenAI Agents SDK.
• Experience with machine-learning algorithms, natural language processing (NLP), deep learning, and vector embeddings.
• Experience with cloud platforms such as Azure, AWS, and GCP, including serverless computing services.
• Familiarity with MLOps tools such as MLflow, Kubeflow, Azure Machine Learning, Amazon SageMaker, or Databricks.
• Experience working with vector databases, including Pinecone, Chroma, FAISS, and Azure AI Search.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.

What We Offer

• Vacation days: Up to 26 business days per year.
• 10 illness/special days off per year (fully paid, no medical papers needed) for all contract types
• Health and life insurance (Luxmed)
• MyBenefit platform with Multisport option
• Internal psychological support service
• English language classes from the first working day
• Access to external learning platforms: O’Reilly, LinkedIn Learning, Udemy, and a wide catalog of diverse internal training
• Flexible workplace: work from the office, from home, or choose a hybrid option
• Tech Skills Mentoring Program
• Opportunities to develop as a public speaker, mentor, or technical interviewer
• Fully paid idle (bench) when not involved in a project
• Certification reimbursement (AWS, GCP, Microsoft, etc.)