Job Responsibilities
- Develop AI-powered applications leveraging LLMs, Generative AI, Agentic AI, and advanced analytics.
- Build and enhance AI agents that interact with enterprise tools, data sources, APIs, and business workflows.
- Develop orchestration workflows using frameworks such as LangGraph, Semantic Kernel, LangChain, or similar technologies.
- Implement MCP (Model Context Protocol) integrations to securely connect AI applications with enterprise systems, knowledge sources, and services.
- Build and support Retrieval-Augmented Generation (RAG), GraphRAG, and Knowledge Graph solutions.
- Develop scalable Python-based applications, APIs, and services to support AI and analytics use cases.
- Integrate enterprise LLM platforms and GenAI services into business workflows and operational processes.
- Perform data exploration, experimentation, testing, and performance optimization across structured and unstructured datasets.
- Create dashboards, reporting solutions, and user-facing applications that translate AI outputs into actionable insights.
- Collaborate with Compliance, Technology, and Business stakeholders to understand requirements and deliver high-quality solutions.
- Contribute to reusable frameworks, engineering standards, testing practices, and AI governance requirements.
Required Qualifications and Skills
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related quantitative discipline.
- 5+ years of experience developing software, automation, analytics, or AI solutions.
- Strong programming skills in Python and SQL.
- Experience working with LLMs, Generative AI platforms, prompt engineering, and AI application development.
- Experience building solutions using orchestration frameworks such as LangGraph, LangChain or similar technologies.
- Understanding of Agentic AI concepts, tool calling, workflow automation, memory management, and multi-step reasoning.
- Experience implementing MCP (Model Context Protocol) integrations or similar enterprise integration patterns.
- Familiarity with RAG, embeddings, vector databases, semantic search, and knowledge retrieval techniques.
- Experience working with REST APIs, JSON, and enterprise system integrations.
- Strong problem-solving, analytical, and communication skills.
- Ability to work independently and collaboratively in a fast-paced environment.
Preferred Qualifications
- Experience with Neo4j, Knowledge Graphs, or GraphRAG architectures.
- Experience with AI observability, evaluation frameworks, and LLM testing methodologies.
- Experience with cloud-based AI platforms and enterprise AI ecosystems.
- Familiarity with compliance, risk management, or financial services domains.
- Experience building AI copilots, assistants, workflow automation solutions, or multi-agent systems.