AI Engineer – Agentic AI & Intelligent Systems

Your Team Responsibilities

As a senior member of the technology team, this role will be responsible for establishing, scaling, and enabling AI capabilities across MSCI. The responsibilities span architecture, delivery, enablement, and governance, with a strong focus on practical, production-ready outcomes.

AI Architecture & Solution Design

• Design and implement enterprise-grade AI solutions leveraging Agentic AI, Retrieval-Augmented Generation (RAG), and AI orchestration frameworks, aligned with MSCI’s technology standards.

• Define reference architectures, patterns, and best practices for integrating AI into existing platforms and applications.

• Ensure AI solutions are scalable, secure, resilient, and compliant with enterprise requirements.

AI Platform & Framework Enablement

• Build and maintain reusable AI components, frameworks, and accelerators to reduce duplication across teams.

• Evaluate and recommend AI tools, models, and platforms based on performance, cost, and suitability for enterprise use.

• Support the adoption of MCP servers, model orchestration, and multi-agent systems where appropriate.

Delivery Acceleration & Proof of Concepts

• Lead AI proof-of-concepts and pilots, transitioning successful initiatives into production-ready solutions.

• Partner with product owners, architects, and engineering teams to embed AI capabilities into projects in the pipeline.

• Provide hands-on support during critical delivery phases to ensure milestones are met.

Developer Enablement & Mentorship

• Mentor and upskill developers on AI concepts, tools, and implementation patterns.

• Enable teams to transition from traditional application development to AI-augmented and AI-driven solutions.

• Promote a culture of responsible and efficient AI usage across the organization.

Governance, Risk, and Responsible AI

• Establish guardrails for responsible AI usage, including data handling, model selection, and validation.

• Work with security, risk, and compliance teams to ensure AI solutions meet regulatory and audit expectations.

• Identify and mitigate risks such as hallucinations, bias, and data leakage.

Stakeholder Collaboration & Advisory

• Act as a trusted AI advisor to technology and business stakeholders.

• Translate business problems into AI-enabled solution approaches.

• Provide technical guidance to leadership on AI feasibility, risks, and delivery impact.

Your Key Responsibilities

• Lead the design and implementation of enterprise-grade AI solutions, with a strong focus on Agentic AI, Retrieval-Augmented Generation (RAG), model orchestration, and MCP server–based architectures.

• Act as the primary AI subject matter expert, guiding teams from idea and proof-of-concept through production deployment.

• Define and maintain AI reference architectures, design patterns, and reusable frameworks to accelerate delivery and ensure consistency across projects.

• Partner with product, architecture, and engineering teams to embed AI capabilities into applications and platforms in the delivery pipeline.

• Evaluate, select, and integrate AI models, tools, and platforms, balancing performance, scalability, cost, and enterprise constraints.

• Establish and enforce responsible AI practices, including governance, security, data privacy, and risk mitigation.

• Mentor and enable developers to adopt AI technologies, lowering the learning curve and increasing overall team productivity.

• Support the migration of traditional solutions to AI-enabled architectures, ensuring minimal disruption to ongoing deliveries.

• Collaborate with stakeholders to translate business problems into practical, AI-driven solutions.

• Contribute to long-term AI strategy by identifying opportunities where AI can improve efficiency, automation, and insight generation.

• Apply data engineering concepts (e.g., data pipelines, data quality, feature preparation, integrations with data platforms) to support AI solutions where required.

Your skills and experience that will help you excel

Core (Must-Have) AI Expertise

• Proven, hands-on experience designing and delivering AI solutions in real-world or enterprise environments.

• Strong expertise in Agentic AI architectures, including multi-agent systems, tool-calling, planning, and orchestration patterns.

• Deep understanding and practical experience with Retrieval-Augmented Generation (RAG), including document ingestion, embedding strategies, vector databases, retrieval optimization, and grounding techniques.

• Experience working with model orchestration frameworks and MCP server–based architectures to enable scalable and modular AI systems.

• Strong knowledge of LLMs, prompt engineering, evaluation techniques, and strategies to mitigate hallucinations and performance issues.

• Experience moving AI solutions from proof-of-concept to production, including monitoring, performance tuning, and lifecycle management.

Engineering & Platform Skills

• Strong software engineering fundamentals, with experience building scalable, maintainable, and secure services.

• Experience integrating AI capabilities into existing applications, APIs, and enterprise platforms.

• Familiarity with cloud-based AI services and deployment patterns.

Data Engineering (Good to Have)

• Working knowledge of data engineering concepts, including data pipelines, data quality, transformations, and integration with analytical platforms.

• Experience preparing and managing data for AI use cases, such as feature preparation, document processing, and data validation.

• Understanding of how data architecture decisions impact AI performance and reliability.

Governance, Security, and Responsible AI

• Experience implementing responsible AI practices, including data privacy, access controls, auditability, and model governance.

• Ability to assess and mitigate risks related to bias, hallucinations, and misuse of AI systems in enterprise environments.

Collaboration, Leadership, and Enablement

• Ability to act as a technical leader and mentor, enabling teams to adopt AI technologies effectively.

• Strong communication skills to translate complex AI concepts into clear, actionable guidance for both technical and non-technical stakeholders.

• Experience influencing architecture and technology decisions without direct authority.

• A mindset focused on reuse, standardization, and long-term sustainability.