Principal AI/ML Architect
Experience: 8–10 Years
Location: Remote
Working Hours: 11:00 AM – 9:00 PM IST
Job Summary
We are looking for a highly experienced Principal AI/ML Architect with strong hands-on expertise in Artificial Intelligence, Machine Learning, Generative AI, Large Language Models (LLMs), RAG, Agentic AI, Python, Cloud, MLOps, and LLMOps.
The ideal candidate will be responsible for designing and delivering enterprise-scale, secure, scalable, and production-ready AI/ML and GenAI solutions. This is a hands-on architecture role requiring the ability to move from solution architecture and technical design to implementation, deployment, monitoring, troubleshooting, and optimization.
The candidate should have strong experience working with enterprise stakeholders and leading technical discussions while remaining actively involved in engineering and delivery.
Key Responsibilities
- Architect, design, and implement end-to-end AI/ML and Generative AI solutions for enterprise use cases.
- Design and develop solutions using LLMs, RAG, embeddings, vector databases, semantic search, prompt engineering, context engineering, and Agentic AI.
- Build production-grade AI/ML applications, APIs, and microservices using Python.
- Design and implement RAG pipelines, including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
- Design AI Agent and multi-agent architectures using appropriate orchestration frameworks.
- Define and implement MLOps/LLMOps practices covering model deployment, inference, evaluation, monitoring, versioning, and lifecycle management.
- Build and maintain CI/CD pipelines for AI/ML and GenAI applications.
- Implement automated testing, model/prompt versioning, observability, tracing, and production monitoring.
- Integrate AI solutions with enterprise applications, APIs, data platforms, databases, and cloud services.
- Design scalable pipelines for both structured and unstructured data.
- Evaluate and optimize AI/ML systems for accuracy, latency, scalability, reliability, performance, and cost.
- Design enterprise AI solutions with appropriate security, privacy, access control, governance, and Responsible AI practices.
- Support production deployments, troubleshoot issues, perform root-cause analysis, and continuously optimize AI systems.
- Lead architecture reviews, design discussions, technical presentations, and code reviews.
- Establish engineering standards and best practices for AI/ML and GenAI development.
- Mentor senior engineers and architects and provide technical leadership across AI/ML initiatives.
- Collaborate closely with product, engineering, data, cloud, security, and business stakeholders.
Mandatory Skills & ExperienceAI/ML & GenAI
- 8+ years of hands-on experience in AI/ML engineering, architecture, or enterprise AI delivery.
- Strong expertise in:
- Machine Learning
- Deep Learning
- NLP
- Generative AI
- Large Language Models (LLMs)
- Strong hands-on experience building LLM-powered applications.
- Hands-on experience with:
- RAG
- Embeddings
- Vector Databases
- Semantic Search
- Prompt Engineering
- Context Engineering
- LLM Evaluation
- Strong understanding of Agentic AI and AI Agent orchestration.
- Experience designing production-grade multi-agent systems is highly valuable.
Python & Application Engineering
- Strong proficiency in Python.
- Experience developing production-grade REST APIs and microservices.
- Strong understanding of enterprise application and API integrations.
- Experience with cloud-native application development and deployment.
MLOps / LLMOps
- Strong hands-on experience with MLOps and/or LLMOps.
- Experience with:
- Model deployment
- Model inference
- Model evaluation
- Model monitoring
- Model lifecycle management
- Prompt/version management
- Automated testing
- Observability
- Experience building CI/CD pipelines for AI/ML applications.
- Strong experience with Docker and cloud-native deployment.
Cloud
Strong hands-on experience with at least one major cloud platform:
- Microsoft Azure
- AWS
- Google Cloud Platform (GCP)
Experience with cloud AI/ML services and managed GenAI platforms is preferred.
Enterprise Architecture
- Experience designing scalable, reliable, secure, and production-ready enterprise AI architectures.
- Strong understanding of:
- AI security
- Data privacy
- Access control
- Governance
- Responsible AI
- Scalability
- Performance optimization
- Reliability
- Cost optimization
- Experience integrating AI solutions with enterprise applications, data platforms, APIs, and cloud infrastructure.
Good to Have
- Azure OpenAI / Azure AI Foundry
- AWS Bedrock
- Google Vertex AI
- LangChain
- LangGraph
- Semantic Kernel
- LlamaIndex
- Multi-agent frameworks and architectures
- Kubernetes
- MLflow
- Kafka / Event-driven architecture
- Pinecone
- Azure AI Search
- Weaviate
- Milvus
- pgvector
- AI evaluation frameworks
- AI red teaming
- Guardrails and AI safety frameworks
- AI observability and tracing
- Fine-tuning / PEFT
- RAG optimization and retrieval evaluation
- Model optimization and inference optimization
Leadership & Collaboration
- Proven ability to provide architecture leadership while remaining hands-on technically.
- Strong experience leading architecture and technical design discussions.
- Ability to mentor engineers and conduct effective code/design reviews.
- Strong troubleshooting and problem-solving capabilities.
- Excellent communication and stakeholder management skills.
- Ability to work effectively with distributed and cross-functional teams.
Candidate Profile
The ideal candidate is a hands-on Principal AI/ML Architect who can independently take an enterprise AI problem from business requirements → architecture → development → deployment → monitoring → optimization.
The candidate should have a proven track record of delivering production-grade AI/ML and Generative AI solutions, with strong expertise in LLMs, RAG, Agentic AI, Python, Cloud, MLOps/LLMOps, enterprise architecture, security, and Responsible AI.
Key Skills
AI/ML Architecture | Generative AI | LLM | RAG | Agentic AI | AI Agents | Python | Vector Database | Embeddings | Semantic Search | Prompt Engineering | Context Engineering | MLOps | LLMOps | Azure | AWS | GCP | REST APIs | Microservices | CI/CD | Docker | Kubernetes | MLflow | AI Security | Responsible AI | Enterprise AI Architecture
Pay: ₹90,000.00 - ₹110,000.00 per month
Experience:
- AI/ML engineering: 8 years (Required)
Work Location: Remote
