Associate - Data Science / Applied AI ML

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.