GEN AI Architect

Key Responsibilities and Duties -

Define GenAI Architecture: Establish the architectural blueprint reference architectures and
technology standards for deploying GenAI solution - including Retrieval-Augmented

Generation (RAG) autonomous agents and model fine-tuning pipelines.

Complete Hands-on in developing Agentic AI applications for production scalable experience
is mandatory.

Technology Selection and Evaluation: Conduct rigorous evaluation benchmarking and
selection of foundational models (both commercial and open-source e.g. GPT Claude
Llama) vector databases (e.g. OpenSearchPinecone Weaviate) and orchestration
frameworks (e.g. LangChain LlamaIndex openAISDK).

Integration Planning: Design robust integration patterns (APIs microservices event-driven
architectures) to seamlessly connect GenAI capabilities with core enterprise platforms (CRM
ERP HRIS) and existing data infrastructure.

Performance and Cost Optimization: Architect technicals with a focus on high-throughput
low-latency inference and optimization of computational resources (GPU/TPU utilization) to
ensure cost-efficiency at enterprise scale.

Responsible AI and Governance: Operationalize and enforce enterprise-wide Responsible AI
policies including mechanisms for bias mitigation toxicity filtering data provenance and
explainability (XAI) within all GenAI deployments.

Data Security and Privacy: Design data workflows and security measures to ensure sensitive
enterprise and customer data is protected throughout the GenAI lifecycle adhering to
regulations such as GDPR

LLMOps Implementation: Define and standardize LLMOps practices including automated
model deployment continuous monitoring for model drift and hallucination version control
and CI/CD pipelines for AI assets.

Innovation Roadmap: Develop and maintain a forward-looking Generative AI technology
roadmap constantly evaluating emerging trends (e.g. multi-modal models agentic
frameworks) and proposing pilots and strategic investments.

Serve as the Generative AI Subject Matter Expert (SME) in engagements with C-level
executives product owners and business unit leaders to define high-impact use cases and
communicate technical risks and trade-offs.

Required Qualifications and Experience

Technical Expertise -
Experience: Minimum of 10 years of experience in Technical Architecture Data Architecture
or ML Engineering with a minimum of 3 years dedicated to architecting production-grade
Generative AI or

Generative AI: Deep hands-on expertise with LLMs Transformer architectures Fine-
Tuning/Transfer Learning and complex techniques like RAG and advanced Prompt
Engineering.

Cloud Platforms: Expert-level proficiency with a major cloud provider (AWS Azure or GCP)
and their respective AI/ML service offerings (e.g. Amazon Bedrock Azure OpenAI Service
Google Vertex AI).

Programming: Mastery of Python including relevant data science and ML libraries (PyTorch
TensorFlow).

Data Systems: Proven experience designing data pipelines for GenAI including vectorization
embedding models and integration with modern data architectures (data lakes data
meshes).

DevOps/MLOps: Strong understanding of containerization (Docker Kubernetes) and MLOps
tools for managing the lifecycle of production AI models.

Ability to work in a dynamic and high-pressure environment with a solution mind-set

Required Skills:

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