01Key Responsibilities
24 years of professional experience in AI/ML Engineering or Applied Data Science.
Proficiency and experience in Python, ML libraries (scikit-learn, XGBoost), and deep learning frameworks (PyTorch, TensorFlow).
Experience with LLMs, transformers (Hugging Face), MCP and RAG frameworks (e.g., LangChain, Haystack).
Hands-on experience with Ollama, Microservices, Docker/Podman, and Kubernetes deployments.
Strong understanding of data transformation, analytics, and pipeline development using tools like Pandas, NumPy, and SQL.
Experience with AI Agents (AutoGPT, CrewAI, or custom orchestration logic) is a strong plus.
Exposure to CI/CD tools (GitLab, ArgoCD, Jenkins), observability (Prometheus, Grafana), and artifact/version tracking (MLflow, Weights & Biases).
Exposure to multi-modal models (text, image, tabular).
Experience with GPU-accelerated inference and model quantization.
Prior experience working in air-gapped or enterprise-restricted network environments.
Familiarity with Elasticsearch, ClickHouse, MongoDB, or vector databases. .
