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., OpenSearch,Pinecone, 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