AI /ML Architect

Techhire Global; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Techhire Global in Bengaluru, India is seeking an AI/ML Architect to lead end-to-end architecture across enterprise AI solutions. The role requires designing multi-component AI systems and ensuring secure, scalable data flows.

You will collaborate with DSA, MLOps, and engineering teams to govern architecture, select cloud and on-prem deployment models, and drive responsible AI practices in line with governance and compliance.

Qualifications

• 8–12 years of total professional experience in software or AI domains.
• 4–5 years designing enterprise AI/ML architectures.
• Proven experience delivering multi-component AI solutions for large-scale data systems.

Responsibilities

• Design end-to-end architectures integrating AI/ML capabilities.
• Define integration standards, APIs, interoperability with existing platforms.
• Oversee data flow, security, privacy, and Responsible AI implementation.
• Architect AI app integrations with current enterprise apps and platforms.
• Evaluate cloud-native and on-prem deployment models.
• Collaborate with Data Science, MLOps, and Engineering for governance.

Skills

AI/ML Architecture

Enterprise AI Solutions

Cloud-Native Architecture

Multi-Cloud Integration

Tools

AWS

Azure

GCP

TensorFlow

PyTorch

HuggingFace

LangChain

REST/GraphQL

Job description

AI/ML Architect

Experience

• Minimum 812 years of total professional experience.
• At least 4-5 years in architecting enterprise-level AI/ML solutions or large-scale data systems.
• Proven experience designing and deploying multi-component AI solutions for enterprise environments.

Key Responsibilities

1. Design and implement end-to-end solution architectures embedding AI/ML capabilities.
2. Define integration standards, APIs, and interoperability frameworks between Al systems and existing Digital India platforms.
3. Oversee data flow architecture, security, privacy, and Responsible AI implementation across solutions.
4. Architect and oversee integration of AI applications with current enterprise applications and digital platforms.
5. Evaluate and recommend cloud-native and on-premises deployment models in alignment with prescribed guidelines.
6. Collaborate with Data Science, MLOps, and Engineering teams for unified architecture governance.

Technical Competencies

• Architecture: Microservices and serverless patterns, event-driven and domain-driven design, high-level data & model lifecycle architecture
• Cloud: AWS (Bedrock, SageMaker, Lambda, API Gateway, S3), Azure (OpenAl Service, ML Studio, AKS), GCP (Vertex AI, BigQuery, Cloud Storage), multi-cloud integration & cost optimization
• AI Frameworks: TensorFlow, PyTorch, Hugging Face Transformers, LangChain (LLM orchestration), prompt-engineering and responsible Al practices
• Data & Storage: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, DynamoDB), vector databases (Pinecone, Weaviate) for RAG & semantic search
• APIs & Integration: REST, GraphQL, WebSocket for real-time chat/voice
• Conversational AI & Voice: Speech-to-Text and Text-to-Speech APIs (AWS Transcribe/Polly, Azure Speech), RAG/chatbot pipeline design with vector stores & LLMs
• Security & Governance: IAM and RBAC across clouds, encryption in transit & at rest, data privacy & AI governance (GDPR, SOC2