Principal AI/ML Architect

Job highlights

• Hands-on experience with RAG,embeddings,vector databases / vector search,prompt engineering,context engineering,and LLM evaluation
• Hands-on experience with MLOps / LLMOps,model deployment,monitoring,CI / CD,and model lifecycle management
• Candidates must submit all required documents and valid identity proofs for verification
• Experience: 8+ Years

Principal AI/ML Architect

Experience: 8+ Years
Work Mode: Remote - Pan India
Working Hours: 11:00 AM - 9:00 PM IST
Budget: 1.5 LPM
Employment Type: Contractual 7 months to 1 year

Immediate joiner required , no dual candidate, BGV mandatory after selection

Role Overview:

We are looking for an experienced Principal AI/ML Architect with strong hands-on expertise in designing, developing, and implementing enterprise-scale AI/ML and Generative AI solutions.

The ideal candidate will combine solution architecture leadership with hands-on technical expertise across Machine Learning, Deep Learning, LLMs, RAG, Agentic AI, Python, and cloud platforms.

The candidate must have proven experience delivering end-to-end, production-ready AI solutions, covering architecture, development, deployment, monitoring, and operationalization.

This role requires a hands-on technical leader who can translate business requirements into scalable, secure, and cost-effective AI solutions while mentoring engineering teams and driving technical excellence.

Key Responsibilities

Architect, design, and implement end-to-end AI, ML, and Generative AI solutions.
Translate business requirements into scalable, secure, and production-ready technical architectures.
Develop and integrate AI/ML solutions using Python, modern AI/ML frameworks, APIs, and cloud services.
Design and implement solutions involving LLMs, Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search, and Agentic AI.
Implement model orchestration, prompt engineering, context engineering, and LLM evaluation strategies.
Build and operationalize model training, deployment, inference, monitoring, evaluation, and lifecycle management capabilities.
Implement MLOps/LLMOps practices, including CI/CD pipelines, model and prompt versioning, automated testing, observability, and monitoring.
Evaluate AI models, frameworks, tools, and cloud platforms based on business and technical requirements.
Integrate AI services with enterprise applications, APIs, data platforms, and cloud infrastructure.
Ensure AI solutions meet enterprise standards for security, privacy, scalability, performance, reliability, and cost optimization.
Troubleshoot complex technical issues and optimize AI/ML application performance.
Provide technical leadership through architecture reviews, hands-on implementation, code reviews, and engineering team mentoring.
Ensure compliance with Responsible AI principles, enterprise governance, and data protection standards.

Required Skills and Experience

8+ years of hands-on AI/ML experience, with proven expertise in architecting, developing, and delivering production-grade enterprise AI/ML solutions.
Strong programming expertise in Python and modern software engineering practices.
Strong knowledge of Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI/LLMs.
Hands-on experience with RAG, embeddings, vector databases/vector search, prompt engineering, context engineering, and LLM evaluation.
Experience designing and implementing Agentic AI solutions and LLM orchestration.
Strong experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
Hands-on experience with MLOps/LLMOps, model deployment, monitoring, CI/CD, and model lifecycle management.
Experience with containers, APIs, microservices, and production application integration.
Knowledge of structured and unstructured data, data pipelines, and enterprise data platforms.
Strong understanding of AI security, data privacy, Responsible AI, and enterprise governance.
Excellent problem-solving, communication, stakeholder management, and technical leadership skills.

Key Expectations

Proven ability to deliver AI/ML solutions end-to-end, from architecture to production deployment.
Strong balance of hands-on coding and solution architecture.
Ability to design scalable, secure, maintainable, and cost-efficient AI systems.
Experience collaborating with cross-functional teams across engineering, architecture, product, and business functions.
Ability to mentor engineers and establish best practices in AI solution development.

Mandatory Documentation and Background Verification (BGV)

Background Verification (BGV) is mandatory.
Candidates must submit all required documents and valid identity proofs for verification.
Complete documentation is mandatory to proceed with the onboarding process.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.