Generative AI Engineer

Key Responsibilities:

1. Technical Leadership & Architecture

• Own the end-to-end technical strategy and architecture for the Agentic AI program, ensuring

scalability, security, and enterprise alignment.

• Lead design and development of intelligent agent workflows leveraging LLMs, reasoning

engines, and autonomous orchestration frameworks (e.g., LangChain, AutoGen, Copilot Studio).

• Establish coding standards, MLOps practices, and integration frameworks to ensure quality and

maintainability.

• Collaborate with solution architects, data engineers, and DevOps to design cloud-native,

production-ready AI systems.

2. Solution Design & Delivery

• Translate business and functional requirements into detailed technical specifications, solution

blueprints, and implementation plans.

• Oversee AI model integration, API development, and data pipeline orchestration.

• Drive experimentation, fine-tuning, and continuous improvement of agentic workflows for

accuracy, responsiveness, and efficiency.

• Ensure delivery timelines, quality standards, and compliance benchmarks are met across all AI

initiatives.

3. Team & Program Leadership

• Mentor and guide engineers, data scientists, and automation developers in building AI-powered

solutions.

• Lead agile sprints, design reviews, and release planning sessions for multiple concurrent

workstreams.

• Partner closely with business analysts, product owners, and stakeholders to ensure alignment

between technical delivery and business value.

• Manage vendor or partner engagements for AI platform integration and model deployments.

4. Innovation, Optimization & Governance

• Evaluate emerging tools, LLM frameworks, and agent orchestration technologies to continuously

enhance solution capabilities.

• Define and monitor performance metrics (e.g., automation rate, accuracy, response latency,

ROI).

• Implement governance practices for AI model explainability, data privacy, and ethical

compliance.

• Champion reusability, modularity, and best practices for sustainable AI engineering.

Qualifications & Skills:

Minimum Qualifications:

• Bachelor's or Master's degree in Computer Science, AI/ML, or a related technical discipline.

• 8–12 years of experience in technology roles with at least 3 years in AI/automation leadership.

• Proven experience in designing and deploying agentic AI or LLM-based systems in enterprise

settings.

• Expertise in Python, APIs, vector databases, and orchestration frameworks (LangChain,

AutoGen, Copilot Studio, or similar).

• Strong understanding of cloud-native AI development (Azure OpenAI, AWS Bedrock, Google

Vertex AI).

• Solid grasp of MLOps, prompt engineering, and retrieval-augmented generation (RAG) patterns.

Experience integrating AI agents with business systems (CRM, ERP, ITSM, or custom workflows nterview.