Senior AI Engineer

Mandatory skill: Expertise in Lang Chain, Llama Index, Semantic Kernel, Auto Gen, or equivalent

• Agent Development: Build and orchestrate autonomous AI agents with multi-step reasoning, tool usage, and workflow chaining using frameworks like Lang Chain, CrewAI, Auto Gen, Semantic Kernel, or Llama Index.
• 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 Lang Chain, Llama Index, Semantic Kernel, Auto Gen, 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.