Description

Qualifications
·       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

Key Skills

Education

Bachelor's degree