AI Engineer

• Agent Development: Build and orchestrate autonomous AI agents with multi-step reasoning, tool usage, and workflow chaining using frameworks like LangChain, CrewAI, AutoGen, Semantic Kernel, or LlamaIndex.
• LLM Integration & Optimization: Deploy, fine-tune, and serve open-source LLMs (e.g., Llama 3) using Databricks Model Serving; optimize latency, throughput, and cost.
• RAG & Knowledge Systems: Design advanced RAG pipelines leveraging vector search, embeddings, semantic ranking, and enterprise data sources (structured + unstructured).
• Context Engineering: Develop prompt strategies, memory frameworks, and metadata tagging to improve contextual accuracy and response quality.
• UI & Experience Design: Build intuitive AI-driven applications using Databricks Apps (Streamlit/Dash) or modern web frameworks to enable business consumption.
• Data Engineering for AI: Build reliable data pipelines (batch & streaming) supporting training, inference, and feature generation using Delta Lake.
• Security & Governance: Implement enterprise-grade controls using Unity Catalog (row/column-level security, lineage, auditability) aligned with compliance standards.
• LLM Guardrails & Responsible AI: Implement guardrails (e.g., NeMo Guardrails) for prompt injection prevention, hallucination mitigation, and safe output handling.
• MLOps & AIOps: Establish CI/CD pipelines for AI models and agents, including versioning, monitoring, drift detection, observability, and incident response.
• Performance & Cost Optimization: Optimize model performance, GPU/compute usage, and inference cost efficiency across environments.
• Testing & Evaluation
• Collaboration & Stakeholder Engagement
• Documentation & Knowledge Transfer

Databricks & Lakehouse

• Strong experience with Unity Catalog, Delta Lake, Vector Search, Databricks Workflows, and Model Serving

• Hands-on with Lakehouse architecture patterns LLMs & Generative AI

• Experience with open-source LLMs (Llama, Mistral, etc.), prompting techniques, and fine-tuning approaches

• Strong knowledge of RAG architectures and embedding strategies AI Engineering & Frameworks

• Expertise in LangChain, LlamaIndex, Semantic Kernel, AutoGen, or equivalent

• Experience building agentic workflows and multi-agent systems Programming

• Advanced Python proficiency (APIs, web apps, orchestration, data processing)

• Familiarity with REST APIs and microservices architecture MLOps & Monitoring

• Experience with MLflow, CI/CD pipelines, model lifecycle management, and observability tools

• Knowledge of drift detection and model performance monitoring Data Engineering Foundations

• Experience with Spark, SQL, and large-scale data processing

• Familiarity with streaming frameworks (Kafka, Structured Streaming) Security & Governance

• Expertise in AI security risks (prompt injection, jailbreaks, data leakage)
• Experience implementing governance frameworks and compliance controls.