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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Generative AI and NLP, with a strong focus on designing and implementing scalable AI systems using Large Language Models. Proficient in collaborating with cross-functional teams to align AI solutions with business objectives while ensuring reliability, governance, and performance optimization.
Highest-signal resume keywords
Generative AI ExpertiseLarge Language Models (LLMs)Python and FastAPIAzure DevOps and CI/CD PipelinesNLP Techniques and Applications
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Generative AINatural Language Processing (NLP)Prompt EngineeringText ClassificationDocument AIPythonFastAPIAzure DatabricksFlaskDocker
Soft Skills
Stakeholder EngagementInfluencing Decision-MakingMentoringCollaborationCommunication
Tools & Technologies
Azure OpenAILangChainDatabricksAzure Cognitive ServicesMLflowAzure Function AppsLangSmithLangFuseWeights & BiasesAzure Document Intelligence
Industry Keywords
SaaS-based LLMsRAG SystemsHybrid RetrievalKnowledge GraphGovernanceCost OptimizationAgentic FrameworksDocument DigitizationOCR MethodsAI Governance
Tech Stack
Tools & technologiesAzureDockerFlaskPythonUnity
About the role
Key responsibilities & impact- Design, develop, and implement advanced solutions leveraging Large Language Models
- Own initiatives and deliver end-to-end solutions with minimal supervision
- Stay current with Generative AI, LLMs, RAG systems, and applied research
- Build and maintain reusable code libraries, tools, frameworks, accelerators, and components
- Participate in code reviews and design reviews
- Contribute across software design, implementation, testing, deployment, and maintenance
- Collaborate with cross-functional teams to align AI solutions with business goals and core systems
- Translate ambiguous business problems into structured AI approaches and measurable outcomes
- Drive solution metrics including productivity gains, automation, decision accuracy, adoption, accuracy, latency, and cost efficiency
- Engage stakeholders, present solutions, influence decision-making, and communicate architecture and trade-offs
- Contribute to client discussions, proofs of concept, and proposal development
- Design scalable, modular, production-grade AI systems, including APIs, pipelines, and orchestration layers
- Define architecture patterns for LLM applications such as RAG pipelines, agentic workflows, and hybrid systems
- Make latency, cost, accuracy, and maintainability trade-offs
- Contribute to internal intellectual property, templates, and reference architectures
- Ensure LLM reliability and robustness through evaluation frameworks, guardrails, and fallback strategies
- Design safe and responsible AI systems, including hallucination mitigation, bias handling, and governance
- Optimize cost-performance trade-offs in large-scale deployments
- Identify when not to use LLMs and propose alternatives
- Mentor junior team members and drive quality standards and best practices
- Evaluate new GenAI advancements for business applicability
- Contribute to internal knowledge sharing, training, and capability building
Requirements
What you’ll need- Experience requirements are not explicitly stated
- Generative AI and NLP expertise, including SaaS-based LLMs, LangChain, LlamaIndex, vector databases, prompt engineering, Azure OpenAI function calling, and multimodal models
- Experience with open-source and SaaS LLMs such as Azure OpenAI, Claude Opus 4.6, GPT-3.5 Turbo, and GPT-4
- At least one agentic Generative AI framework: CrewAI, AutoGen, LangGraph, n8n, LangFlow, SmolAgents, or Semantic Kernel
- Advanced RAG systems, including hybrid retrieval, knowledge graph-based retrieval, multi-hop RAG, hierarchical/contextual retrieval, and RAG evaluation/monitoring
- Classical NLP: text classification, topic modeling, Q&A systems, conversational AI/chatbots, search, Document AI, summarization, content generation, and NER
- Databricks ecosystem: Databricks Genie, Databricks AI/BI, and AgentBricks
- Microsoft Copilot Studio and knowledge of no-code/low-code app development
- MCP server, tools, skills, and reusable component creation and maintenance
- Python and FastAPI
- Azure DevOps and Agile/Azure Boards
- Azure Databricks, MLflow Model Lifecycle Management, and Unity Catalog
- Azure Function Apps, Azure Blob Storage, Azure Cognitive Services, and Azure AI Search
- Agent monitoring, evaluation, and debugging frameworks
- LLM observability and tracing using LangSmith, LangFuse, or Weights & Biases
- Prompt/version management and experimentation
- Governance, compliance, and cost optimization for LLMs
- CI/CD pipelines in Azure DevOps
- Flask and Docker
- Document digitization and OCR methods
- Azure Document Intelligence or equivalent
- Azure Delta Lake
- Willingness to contribute to ad-hoc initiatives, non-GenAI AI/ML projects, and business-context learning