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Role Overview
Essential Skills - Azure AI Knowledge is must
The AI Tech Lead is responsible for architecting designing and leading end to end AI/GenAI solutions across enterprise programs. This role combines deep technical expertise with leadership to drive AI adoption guide technical teams collaborate with business stakeholders and ensure scalable secure and compliant AI delivery. The AI Tech Lead acts as the primary technical authority for AI initiatives within the program/CoE.
Key Responsibilities
1. AI Solution Architecture & Design
Lead the architecture and design of AI/ML/GenAI solutions (LLMs prompt engineering vector search RAG NLP computer vision MLOps automation).
Evaluate and select appropriate AI frameworks models (open-source & proprietary) cloud services and data pipelines.
Define scalable architectures using Azure AWS GCP or hybrid environments.
Drive RAG pipelines model fine tuning and embeddings strategy for enterprise use cases.
2. Technical Leadership
Mentor and guide a team of AI engineers data scientists ML engineers analysts and automation engineers.
Conduct code reviews design reviews and ensure best practices in AI and software engineering.
Build reusable AI components accelerators and frameworks for the organization.
3. Delivery & Execution
Translate business requirements into technical specifications and AI solution approaches.
Lead POCs pilots and AI product lifecycle from ideation to deployment.
Ensure accuracy performance and reliability of AI systems through evaluations testing and optimizations.
Coordinate with DevOps/MLOps teams for CI/CD pipelines model deployment monitoring and observability.
4. Data Engineering & Governance
Work closely with data engineering teams to define data ingestion cleansing feature engineering and model training datasets.
Ensure compliance with enterprise data governance privacy security and ethical AI guidelines.
Set up monitoring systems for model drift data drift and performance degradation.
5. Stakeholder Management
Collaborate with business leaders product owners architects QA and Validation leads and delivery partners.
Communicate technical concepts in a simple business-friendly manner.
Prepare architecture documents solution blueprints and presentations for leadership reviews. | |
: Role Overview Essential Skills - Azure AI Knowledge is must The AI Tech Lead is responsible for architecting designing and leading end to end AI/GenAI solutions across enterprise programs. This role combines deep technical expertise with leadership to drive AI adoption guide technical teams co...
| :
Role Overview
Essential Skills - Azure AI Knowledge is must
The AI Tech Lead is responsible for architecting designing and leading end to end AI/GenAI solutions across enterprise programs. This role combines deep technical expertise with leadership to drive AI adoption guide technical teams collaborate with business stakeholders and ensure scalable secure and compliant AI delivery. The AI Tech Lead acts as the primary technical authority for AI initiatives within the program/CoE.
Key Responsibilities
1. AI Solution Architecture & Design
Lead the architecture and design of AI/ML/GenAI solutions (LLMs prompt engineering vector search RAG NLP computer vision MLOps automation).
Evaluate and select appropriate AI frameworks models (open-source & proprietary) cloud services and data pipelines.
Define scalable architectures using Azure AWS GCP or hybrid environments.
Drive RAG pipelines model fine tuning and embeddings strategy for enterprise use cases.
2. Technical Leadership
Mentor and guide a team of AI engineers data scientists ML engineers analysts and automation engineers.
Conduct code reviews design reviews and ensure best practices in AI and software engineering.
Build reusable AI components accelerators and frameworks for the organization.
3. Delivery & Execution
Translate business requirements into technical specifications and AI solution approaches.
Lead POCs pilots and AI product lifecycle from ideation to deployment.
Ensure accuracy performance and reliability of AI systems through evaluations testing and optimizations.
Coordinate with DevOps/MLOps teams for CI/CD pipelines model deployment monitoring and observability.
4. Data Engineering & Governance
Work closely with data engineering teams to define data ingestion cleansing feature engineering and model training datasets.
Ensure compliance with enterprise data governance privacy security and ethical AI guidelines.
Set up monitoring systems for model drift data drift and performance degradation.
5. Stakeholder Management
Collaborate with business leaders product owners architects QA and Validation leads and delivery partners.
Communicate technical concepts in a simple business-friendly manner.
Prepare architecture documents solution blueprints and presentations for leadership reviews. | |
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Job Location:
Monthly Salary:
Not Disclosed
Posted on:
11 hours ago
Vacancies:
1 Vacancy
