Key Responsibilities:
AI/ML & LLM Development
- Build, fine-tune, and optimize LLM-based applications using open-source and proprietary models.
- Implement prompt engineering, retrieval-augmented generation (RAG), and evaluation pipelines.
- Develop multi-agent systems with logical coordination, communication flows, and tool integrations.
Multi-Agent & Orchestration Frameworks
- Design AI workflows using LangChain, LlamaIndex, CrewAI, or equivalent frameworks.
- Implement autonomous agents, tool-calling strategies, memory modules, and context management.
- Build scalable agent-to-agent communication pipelines for enterprise use cases.
Data Engineering & Applied Science
- Build robust data ingestion, processing, and feature engineering workflows in Python.
- Perform applied ML research and experimentation on structured, unstructured, and multimodal data.
- Create data-driven insights, predictive models, and performance reports.
Software Engineering & Version Control
- Write clean, modular, production-ready Python code following strong software engineering principles.
- Use Git/GitHub for versioning, branching, reviews, CI/CD pipelines, and release management.
- Collaborate with cross-functional teams for integration and deployment.
Cloud Services & Enterprise Deployment
- Develop and deploy AI services on Azure App Service, AKS, Azure Functions, Azure OpenAI, and Azure Storage.
- Work with scalable architectures: microservices, serverless, and containerized (Docker) applications.
- Ensure enterprise-grade security, compliance, logging, monitoring, and observability.
AI Agents & Autonomous Workflows
- Design agent-based solutions for automation, RAG, decision-making, and workflow execution.
- Implement multi-agent architectures using planning, reasoning, tool usage, and memory.
- Integrate agents with enterprise apps, APIs, Databricks, ADLS, n8n, or custom AI tools.
Collaboration & Stakeholder Engagement
- Work closely with product owners, architects, and business stakeholders.
- Translate business requirements into technical solutions and present AI-driven proposals.
- Document architecture, workflows, and model behaviors clearly.
