Process Manager

Role Overview:

The Tech Lead Agentic AI will spearhead the design, development, and deployment of agentic AI solutions that transform enterprise operations and decision-making. This role requires deep technical expertise in AI system architecture, LLM orchestration, and automation frameworks, combined with leadership capabilities to guide multi-disciplinary teams and deliver high-impact AI programs.

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:

Bachelors or Masters degree in Computer Science, AI/ML, or a related technical discipline. 812 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