Sr AI Engineer - VCC

Responsibilities

1. Digital Process Transformation & Agentic Design

• Analyze legacy business workflows across IT, operations, and corporate functions to identify transformation opportunities.
• Design goal-driven agents that decompose manual tasks into automated sub-tasks.
• Implement advanced patterns: planner–executor, coordinator–worker, reflection, and human-in-the-loop decision gates.

2. Multi-Platform Implementation

• Microsoft Copilot Studio: Build enterprise-grade copilots with custom plugins.
• AWS Bedrock & Dataiku: Operationalize agents within larger data analytics and ML pipelines.

3. Enterprise Knowledge Retrieval and Grounding (RAG) & Graph RAG

• Design and implement RAG pipelines (ingestion, chunking, embeddings) to digitize and make enterprise knowledge accessible to AI agents.
• Develop advanced GraphRAG architectures that leverage knowledge graphs for enhanced context linking and semantic retrieval, enabling agents to reason over interconnected enterprise data.
• Integrate agent-based actions that allow AI systems to autonomously navigate, query, and update knowledge graphs, supporting dynamic workflows and decision-making processes.
• Implement entity and relationship extraction to enrich knowledge graphs, ensuring agents can ground responses in up-to-date, structured enterprise information.
• Ensure retrieval mechanisms respect role-based access control and data classification, enforcing security and compliance throughout the knowledge lifecycle.
• Collaborate with stakeholders to define agent actions triggered by graph-based insights, such as automated reporting, escalation of workflows, or cross-system notifications.

4. Optimization and Machine Learning Model Implementation

• Design and deploy Operations Research (OR) models to optimize business processes, resource allocation, and decision-making workflows across enterprise functions.
• Develop and integrate machine learning (ML) models for tasks such as predictive analytics, anomaly detection, and process automation, ensuring models are aligned with business objectives and data governance standards.
• Collaborate with cross-functional teams to identify use cases where OR and ML models can drive measurable value and translate business requirements into robust technical solutions.
• Continuously monitor, evaluate, and refine model performance, leveraging feedback loops and real-world data to improve accuracy, scalability, and operational impact.
• Ensure seamless integration of OR and ML models within existing enterprise platforms and agentic workflows, supporting end-to-end automation and intelligence-driven transformation.

5. Enhancement of Publicly Available Models

• Evaluate the capabilities and limitations of publicly available large language models (LLMs) and open-source AI tools to determine their suitability for enterprise integration.
• Fine-tune and adapt public models using proprietary or domain-specific data to improve accuracy, relevance, and alignment with organizational goals while ensuring data privacy and compliance.
• Contribute to the broader AI community by identifying opportunities for open collaboration, sharing enhancements, and supporting responsible model stewardship.
• Continuously monitor advancements in the open-source and public model landscape, integrating new developments to maintain competitive advantage and innovation.

6. Security, Governance, and Responsible AI

• Enforce safe action boundaries and prompt-injection defenses for agents interacting with corporate data.
• Design human approval checkpoints for high-risk digital actions.
• Ensure all AI-driven transformations comply with enterprise ethical requirements.

Requirements

• 5+ years professional software engineering experience with relevant experiences and expertises in the above-mentioned responsibilities
• Able to conduct extensive research to achieve project/product outcomes