AI Engineer

Join our teamJoin our team as an AI Engineer.

Your Responsibilities

• Implement automated MLOps pipelines (CI/CD, data, training).
• Implement ETL pipelines for data ingestion.
• Containerize and deploy models into production.
• Continuously monitor models (drift detection, alerting).
• Manage model and data versioning to ensure reproducibility.
• Apply governance principles (bias, privacy, transparency).
• Collaborate with data scientists to transform prototypes into stable services.
• Write optimized prompts and conduct comparative evaluations of models.

You Stand Out With

• Languages & Libraries: Proficiency in Python + AI libraries (Pandas, Huggingface, OpenAI, etc.) and experience with Java and JavaScript.
• AI Agentic Frameworks: Experience with techniques such as multi-agent systems, ReAct, function Autogen, LangGraph, CrewAI, Chainlit, Streamlit, n8n, Google ADK.
• Knowledge of LLM and LFM Models: Familiarity with proprietary models (OpenAI, Claude, Gemini, etc.) and open-source models on HuggingFace.
• Data Science: Strong general understanding of data science techniques and their pipelines.
• Software Architecture: Understanding of distributed systems architecture, microservices, APIs (e.g., REST).
• Cloud Computing: Experience with AWS Bedrock, Azure AI Foundry, GCP Vertex AI.
• DevOps / MLOps: o Deployment via CI/CD, containers (Docker, Kubernetes), cloud, and automated pipelines. o Automation of ML workflows (preprocessing, training, evaluation, deployment). o Versioning of models/data/experiments (MLflow, DVC, etc.). o Monitoring of models in production (drift, latency, performance, business metrics).
• Governance & Compliance: Knowledge of ethics, bias, GDPR, explainability, privacy, AI risks.
• Prompt Engineering & Model Benchmarking: Ability to formulate effective prompts, compare models, test, and select for specific tasks.
• Deployment & Integration: Packaging models, production deployment (API, microservices), backend/legacy integration.
• Communication: Collaborate with data, product, and infrastructure teams; clearly explain AI challenges.

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