DWS
AIEngineer(GenerativeAI/LLMPlatform&Enablement),AVP
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Engineer (Generative AI / LLM Platform & Enablement), AVP at DWS. Skills: Generative AI, LLM Platform, Platform Engineering, Applied GenAI. Design reusable platform components. Develop reusable platform components”
What You'll Achieve.
Widely adopted GenAI platform capabilities; Widely adopted reusable patterns; Improve time-to-value for AI use cases; Consistent application of prompting standards; Consistent application of RAG/CAG patterns; Consistent application of evaluation practices; Adoption of developer AI tooling; Adoption of end-user AI tooling
Industry & Context.
Identify high-impact use cases
What They're Looking For.
Must Have
software engineering foundation, Hands-on experience with LLM applications, Solid, practical understanding of prompt engineering, Solid, practical understanding of RAG / CAG architectures, Solid, practical understanding of Evaluation approaches for GenAI systems, Solid, practical understanding of Observability and operationalisation of AI solutions
Nice to Have
Familiarity with other modern development stacks
What You'll Do.
Design reusable platform components
Develop reusable platform components
Design RAG and CAG patterns
Implement RAG and CAG patterns
Establish reusable prompting frameworks
Establish prompting templates
Establish prompting standards
Demonstrate understanding of Generative AI core concepts
Translate concepts into implementations
Translate concepts into reusable patterns
Leverage end-user oriented tools
Integrate end-user oriented tools
Leverage developer-oriented frameworks
Integrate developer-oriented platforms
Contribute to engineering stacks
Promote coding copilots
Help teams maximize value
Guide users towards solutions
Create reusable assets
Create reference architectures
Create best practices
Communicate technical concepts
Implement evaluation frameworks
Implement observability frameworks
Implement quality frameworks
Ensure solutions meet standards
Align with requirements
How You'll Work.
Team & Collaboration
Trusted advisor to engineers; Trusted advisor to business users; Work across ecosystems; Enablement and consulting mindset; Stakeholder engagement skills
Communication Scope
Communicate complex concepts
Process & Methodology
Agile
Full Job Description
## _**Job Description:**_ **Job Title: AI Engineer (Generative AI / LLM Platform & Enablement), AVP** **Location: Bangalore, India** **Role Description** * As an AI Engineer in the AI Enablement Team, you will design and deliver enterprise‑grade capabilities that enable teams to adopt Generative AI safely and effectively. You will operate across platform engineering and applied GenAI development, while acting as a trusted advisor to engineers and business users—translating emerging AI capabilities into practical, scalable solutions that drive real business impact. **What we’ll offer you** As part of our flexible scheme, here are just some of the benefits that you’ll enjoy * Best in class leave policy * Gender neutral parental leaves * 100% reimbursement under childcare assistance benefit (gender neutral) * Sponsorship for Industry relevant certifications and education * Employee Assistance Program for you and your family members * Comprehensive Hospitalization Insurance for you and your dependents * Accident and Term life Insurance * Complementary Health screening for 35 yrs. and above **Your key responsibilities** Build and Evolve the GenAI Platform * Develop reusable platform components for LLM access, orchestration, and agent-based workflows. * Design and implement **RAG and CAG patterns** , including ingestion pipelines, retrieval strategies, and context assembly to ensure high-quality grounded outputs. * Establish reusable **prompting frameworks** , templates, and standards to enable consistent and scalable use of GenAI across the organization. Apply GenAI Fundamentals in Practice * Demonstrate strong understanding of **Generative AI core concepts** , including: * Prompt engineering (structured prompting, system prompts, optimization) * Retrieval-Augmented Generation (**RAG**) and Context-Augmented Generation (**CAG**) * Programmatic prompting approaches (e.g., **DSPy**) * Translate these concepts into robust, production-ready implementations, and reusable pat
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