AI/ML Solution Architect
LiuS
- Location
- New Delhi, Delhi, India
- Job type
- Full-time
Required skills
- LangChain
- Open Source
- AWS
- Artificial Intelligence
- Azure
- BigQuery
- compliance
- data science
- DynamoDB
- end-to-end
- GCP
- GraphQL
- Lambda
- microservices
- MySQL
- NoSQL
- PostgreSQL
- Serverless
- SQL
- TensorFlow
- Pytorch
- Vertex
About the role
Website:
lius.in
Job details:
Educational Qualification:
- B.Tech / M.Tech / MS / Ph.D. in Computer Science, Information Technology, Artificial Intelligence, or a related technical field.
- Certifications preferred: AWS Solutions Architect Professional, Azure Solutions Architect Expert, or GCP Professional Cloud Architect.
- Research papers, case studies, or significant open source contributions (are preferred)
Experience:
- Minimum 8–12 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 government or enterprise environments.
Key Responsibilities:
- Design and implement end-to-end solution architectures embedding AI/ML capabilities into NeGD and allied government applications.
- Define integration standards, APIs, and interoperability frameworks between AI systems and existing Digital India platforms.
- Oversee data flow architecture, security, privacy, and Responsible AI implementation across solutions.
- Architect and oversee integration of AI applications with NeGD’s current enterprise applications and digital platforms.
- Evaluate and recommend cloud-native and on-premises deployment models in alignment with MeitY guidelines.
- Provide architectural leadership, ensuring scalability, maintainability, and compliance with MeitY and NIC technical standards.
- Collaborate with Data Science, MLOps, and Engineering teams for unified architecture governance.
- Conduct periodic architectural reviews and provide technical mentoring to development teams.
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 (OpenAI 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 AI 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)
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