Key Responsibilities
• 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
Required Skills and Qualifications
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.