Senior Python Engineer (AI/LLM, MLOps)

Quick Facts

• Role: Senior Python Engineer with AI exposure (AI/ML, LLMs, RAG, MLOps)

Description

Design, build, and deploy AI and ML solutions, including large language models and Retrieval Augmented Generation (RAG) pipelines, to automate processes and improve customer and employee experiences. Build scalable AI services and microservices in Python, productionize models with MLOps/CI-CD, and optimize for performance, accuracy, and cost. Integrate AI with enterprise applications and ensure security, compliance, and responsible AI standards.

Responsibilities

• Design, build, and deploy machine learning and generative AI models (LLMs, embeddings, transformers, RAG pipelines)

• Develop scalable AI services and microservices using Python and REST APIs in cloud-native environments

• 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 on ingestion, storage, and governance

• Implement CI/CD pipelines for ML models and MLOps workflows

• Monitor model performance and drift; implement retraining strategies

• Manage model lifecycle with logging and observability

• Integrate AI systems with enterprise applications, APIs, and cloud platforms (Azure, AWS, GCP)

• Build RAG architectures using vector databases (Pinecone, FAISS, Weaviate, Azure AI Search)

• Ensure solutions align with enterprise security, compliance, and responsible AI standards

• Translate product/business requirements into technical solutions and communicate AI capabilities/limitations

• Run proofs of concept (POCs) and support conceptual solution design

Requirements

• Strong proficiency in Python, including NumPy, Pandas, PyTorch, TensorFlow, and Transformers

• Hands-on LLM experience with OpenAI, Azure OpenAI, Anthropic, and Llama models

• Experience with Agentic AI platforms such as AWS Bedrock/AgentCore, Google Vertex AI, Azure AI Foundry, or similar platforms

• Experience with agentic AI frameworks: LangChain, LangGraph, CrewAI, AutoGen, or OpenAI Agents SDK

• Experience with ML algorithms, NLP, deep learning, and vector embeddings

• Cloud experience with Azure, AWS, and GCP, including serverless computing

• Familiarity with MLOps tools: MLflow, Kubeflow, Azure Machine Learning, Amazon SageMaker, or Databricks

• Experience with vector databases: Pinecone, Chroma, FAISS, and Azure AI Search

• Knowledge of containerization and orchestration: Docker and Kubernetes

Benefits

• Up to 26 business vacation days per year

• 10 illness/special paid days off per year (no medical papers required) for all contract types

• Health and life insurance (Luxmed)

• MyBenefit platform with a 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 diverse internal training

• Flexible workplace: office, remote, or hybrid

• Tech Skills Mentoring Program

• Opportunities to develop as a public speaker, mentor, or technical interviewer

• Fully paid idle (bench) when not assigned to a project

• Certification reimbursement (AWS, GCP, Microsoft, etc.)