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Technical Delivery Team Manager-Generative AI Solutions

2 weeks ago

Hyderabad, Telangana, India Pronix Inc Full time

Technical Delivery Lead/Manager – Generative AI Solutions

Location: Gachibowli, HYD

Duration: Full Time

Position Overview

We are seeking a highly skilled and experienced Technical Delivery Lead/Manager – Generative AI Solutions to drive the end-to-end delivery of AI-powered business automation, conversational AI, and generative AI initiatives. This role requires a strong blend of technical expertise, project/program management, and stakeholder leadership to ensure the successful design, development, deployment, and scaling of AI solutions for enterprise clients.

The ideal candidate is a delivery-focused professional with deep knowledge of AI/ML platforms, Generative AI models (LLMs), Conversational AI frameworks, cloud ecosystems , Azure, AWS, GCP), and enterprise integrations. They will play a critical role in ensuring business outcomes are met, projects are delivered on time and within budget, and AI solutions provide measurable ROI for clients.

Delivery Leadership

Lead the technical delivery of Generative AI, Conversational AI, and AI Automation projects across multiple client engagements.

Define delivery roadmaps, project plans, and execution strategies in alignment with client goals and organizational priorities.

Manage cross-functional teams including data scientists, AI engineers, solution architects, and business analysts to ensure high-quality outcomes.

Act as the point of escalation for technical and delivery challenges, ensuring timely resolution.

Technical Expertise & Solutioning

Provide architectural guidance and solution design for Generative AI use cases such as knowledge assistants, AI agents, intelligent automation, and AI-driven CX/EX solutions.

Oversee development and deployment of solutions using platforms like , Azure OpenAI, AWS Bedrock, SAP AI, or custom LLM fine-tuning frameworks.

Ensure integration of AI solutions with enterprise applications (CRM, ERP, ITSM, Data Lakes, APIs, etc.).

Drive best practices for AI model governance, prompt engineering, performance optimization, and scalability.