AI Engineer

• Design & deliver GenAI solutions: Architect and implement LLM/LVM applications (text and, where applicable, vision) with strong emphasis on Microsoft Azure AI Foundry capabilities and LangGraph-based agentic workflows. This includes advanced prompt strategies, guardrails, evaluation metrics, cost/latency optimization, and production rollout.

• Build robust RAG systems: Stand up end-to-end RAG pipelines (ingestion * chunking * embedding * retrieval * synthesis) leveraging Foundry orchestration and LangGraph agents, with observability, feedback loops, and AB testing for groundedness and hallucination control.

• Develop agentic workflows: Implement multi-step, tool-using agents using LangGraph for real-time, context-aware operations; orchestrate planning, memory, and tool calling with safe execution policies.

• Fine-tune foundation models: Select, adapt, and fine-tune open and hosted models for domain-specific tasks using efficient techniques (LoRA/QLoRA, PEFT, parameter-efficient adapters), and manage evaluation datasets.

• ML/LLM engineering: Build high-quality Python code, reusable libraries, and APIs; implement CI/CD, testing, experiment tracking, and model/version governance.

• Data & platforms: Partner with data engineering to operationalize pipelines on enterprise platforms (e.g., Databricks/Snowflake) and integrate with cloud AI/ML services.

Microsoft Foundry & Foundry Agents, or LangGraph Expertise

• Azure AI Foundry: Hands-on experience in designing, deploying, and managing GenAI solutions using Foundry orchestration and governance.

• LangGraph: Proven ability to build agentic workflows, multi-step reasoning, and tool orchestration using LangGraph for enterprise-grade applications.

Generative AI Expertise

• Large Language/Vision Models (LLM/LVM): Hands-on with multiple providers/models (e.g., Gemini, GPT, Claude, Llama) and their APIs.

• Retrieval Augmented Generation (RAG): Ability to design and implement robust RAG systems for real-time, context-aware applications.

• Prompt engineering & agentic workflows: Advanced prompt design (system/task/reflection patterns) and building multi-step AI agents.

• Vector databases/search & embeddings: Practical experience with vector indexing, similarity search, and embedding selection/management.

Mathematics & Foundations

• Statistics, Multivariate Calculus, Linear Algebra, Optimization (you can explain choices and trade-offs in model behavior based on these principles).

Programming

• Advanced Python (clean architecture, typing, packaging, testing; performance profiling and async where appropriate).

Python Ecosystem

• Data Handling/Visualization: pandas, NumPy, Seaborn/Matplotlib, PySpark

• Machine Learning: scikit-learn, XGBoost, LightGBM

• Deep Learning: TensorFlow or PyTorch

• Generative AI: LangChain, LlamaIndex, Haystack, Hugging Face transformers

Software Craftsmanship & Platforms

• Version Control: Git (GitHub/GitLab/Bitbucket)

• Databases: SQL and NoSQL (e.g., MongoDB, Cassandra)

• Cloud: Hands-on with one or more of AWS, Azure, GCP, specifically AI/ML & data services (e.g., AWS SageMaker, Azure Machine Learning, Google Vertex AI)

• Enterprise data engineering platforms: Databricks, Snowflake