YASH Technologies
We at Yash Technologies are looking for
AI Architect , if you are looking for a new opportunity, please share your updated resume.
Location:
Chicago, IL (3 days onsite)
A highly experienced
AI Architect
with 12+ years of professional experience in
designing, building, and scaling enterprise-grade AI and Generative AI (GenAI) solutions
across leading public cloud platforms (Azure, AWS, GCP). The ideal candidate will play a strategic role in
AI solution architecture, data & model engineering, cloud integration, and MLOps/LLMOps frameworks , while working closely with business and technology teams to drive measurable outcomes through AI innovation.
Key Responsibilities
Design and architect scalable, secure, and high‑performing AI/GenAI solutions leveraging cloud‑native services and frameworks.
Integrate AI systems with enterprise data platforms, applications, APIs, and workflow systems ensuring end‑to‑end traceability and performance.
GenAI Solution Design:
Architect and implement Generative AI solutions using LLMs (OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Gemini, etc.) for use cases such as knowledge retrieval, document summarization, copilots, and content generation.
MLOps & LLMOps:
Define and implement model lifecycle management pipelines (CI/CD for ML/LLMs governance, observability, and responsible AI frameworks).
Data Architecture Collaboration:
Work closely with Data Engineering teams to design and optimize feature stores, vector databases, embeddings, and multimodal data pipelines.
Stakeholder Engagement:
Collaborate with business stakeholders, product managers, and delivery teams to translate business objectives into technical AI solutions and architectures.
Evaluate emerging AI technologies, models, and frameworks to recommend fit‑for‑purpose solutions that align with enterprise AI strategy.
Performance Optimization:
Drive optimization of model performance, inference latency, cost, and accuracy across cloud environments.
Guide data scientists and ML engineers in solution design, best practices, and architectural patterns.
GenAI & LLMs:
Experience with GPT‑4/4o, Claude, Gemini, Mistral, LLaMA, fine‑tuning, RAG (Retrieval‑Augmented Generation), and prompt engineering.
MLOps & LLMOps:
Azure ML Pipelines, SageMaker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI.
Data & Integration:
Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL APIs, and vector DBs like Pinecone, Weaviate, FAISS, Milvus, or Cosmos DB.
Programming:
Responsible AI:
Understanding of bias detection, model explainability, and governance principles.
Architecture Design:
Expertise in TOGAF/Well‑Architected frameworks and AI reference architectures.
Qualifications Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
12+ years of total experience, with at least 4‑5 years in AI/ML solution architecture roles.
Proven experience in
designing and delivering AI solutions at enterprise scale .
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer) preferred.
Strong communication and presentation skills for stakeholder engagement and executive alignment.
Preferred Attributes
Demonstrated success in implementing GenAI copilots, chatbots, or RAG systems.
Ability to balance innovation with pragmatic business value delivery.
Passion for continuous learning and AI community engagement.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Information Technology
Industries
IT Services and IT Consulting
Referrals increase your chances of interviewing at YASH Technologies by 2x.
#J-18808-Ljbffr
AI Architect , if you are looking for a new opportunity, please share your updated resume.
Location:
Chicago, IL (3 days onsite)
A highly experienced
AI Architect
with 12+ years of professional experience in
designing, building, and scaling enterprise-grade AI and Generative AI (GenAI) solutions
across leading public cloud platforms (Azure, AWS, GCP). The ideal candidate will play a strategic role in
AI solution architecture, data & model engineering, cloud integration, and MLOps/LLMOps frameworks , while working closely with business and technology teams to drive measurable outcomes through AI innovation.
Key Responsibilities
Design and architect scalable, secure, and high‑performing AI/GenAI solutions leveraging cloud‑native services and frameworks.
Integrate AI systems with enterprise data platforms, applications, APIs, and workflow systems ensuring end‑to‑end traceability and performance.
GenAI Solution Design:
Architect and implement Generative AI solutions using LLMs (OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Gemini, etc.) for use cases such as knowledge retrieval, document summarization, copilots, and content generation.
MLOps & LLMOps:
Define and implement model lifecycle management pipelines (CI/CD for ML/LLMs governance, observability, and responsible AI frameworks).
Data Architecture Collaboration:
Work closely with Data Engineering teams to design and optimize feature stores, vector databases, embeddings, and multimodal data pipelines.
Stakeholder Engagement:
Collaborate with business stakeholders, product managers, and delivery teams to translate business objectives into technical AI solutions and architectures.
Evaluate emerging AI technologies, models, and frameworks to recommend fit‑for‑purpose solutions that align with enterprise AI strategy.
Performance Optimization:
Drive optimization of model performance, inference latency, cost, and accuracy across cloud environments.
Guide data scientists and ML engineers in solution design, best practices, and architectural patterns.
GenAI & LLMs:
Experience with GPT‑4/4o, Claude, Gemini, Mistral, LLaMA, fine‑tuning, RAG (Retrieval‑Augmented Generation), and prompt engineering.
MLOps & LLMOps:
Azure ML Pipelines, SageMaker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI.
Data & Integration:
Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL APIs, and vector DBs like Pinecone, Weaviate, FAISS, Milvus, or Cosmos DB.
Programming:
Responsible AI:
Understanding of bias detection, model explainability, and governance principles.
Architecture Design:
Expertise in TOGAF/Well‑Architected frameworks and AI reference architectures.
Qualifications Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
12+ years of total experience, with at least 4‑5 years in AI/ML solution architecture roles.
Proven experience in
designing and delivering AI solutions at enterprise scale .
Cloud certifications (Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer) preferred.
Strong communication and presentation skills for stakeholder engagement and executive alignment.
Preferred Attributes
Demonstrated success in implementing GenAI copilots, chatbots, or RAG systems.
Ability to balance innovation with pragmatic business value delivery.
Passion for continuous learning and AI community engagement.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Information Technology
Industries
IT Services and IT Consulting
Referrals increase your chances of interviewing at YASH Technologies by 2x.
#J-18808-Ljbffr