Senior AI / ML Engineer
Role details
Job location
Tech stack
Requirements
Python Gen AI. LLM, RAG. Agentic AI nice to have. Azure, but will accept AWS or GCP., 4-year University degree Five or more years of experience in Information Technology Programming: Expert in Python and SQL; strong software engineering practices (testing, patterns, performance). Classical ML: supervised/unsupervised learning, model evaluation, feature engineering, time series. Deep Learning: PyTorch or TensorFlow, transformers, CV/NLP pipelines. Generative AI: LLMs, RAG, fine-tuning, prompt design, evaluation metrics and guardrails. Agentic AI: Practical experience with concepts such as tool-calling, reasoning loops, task planning or multi-agent orchestration (e.g., AutoGen, LangChain Agents, LangGraph) Data processing: Spark/Databricks or equivalent; batch and streaming (e.g., Kafka). Storage: relational and NoSQL; data lakes; vector databases (e.g., FAISS, Pinecone, Weaviate). CI/CD (e.g., GitHub Actions, GitLab CI), containerization (Docker), orchestration (Kubernetes). Experiment tracking and model management (e.g., MLflow, Weights & Biases, DVC). Cloud: Proficiency with one major cloud (AWS, GCP, or Azure) for training and serving (e.g., SageMaker, Vertex AI, AKS). Security and Privacy: Experience handling sensitive data (PII), encryption, access controls, secure model serving. Search and retrieval: Elastic/OpenSearch, knowledge graphs, advanced RAG patterns. Ethics and Compliance: Champions responsible AI and governance. Delivery: On-time, high-quality deployment of ML/LLM features into production. Assess current AI/ML assets, data pipelines, and platform maturity; identify quick wins and strategic gaps.