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: Deployment via CI/CD, containers (Docker, Kubernetes), cloud, and automated pipelines.

Automation of ML workflows (preprocessing, training, evaluation, deployment).

Versioning of models/data/experiments (MLflow, DVC, etc.).

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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