SR AI/ML Engineer

Mazosolutions; Chennai District, Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

MazoSolutions in Chennai seeks a Senior AI/ML Engineer to lead architecture and hands-on development of enterprise AI/ML solutions. You will drive end-to-end delivery, leveraging Python, ML frameworks, and cloud services to build production-grade systems.

You will work with LLMs, RAG, embeddings, vector search, and model orchestration, ensuring security, scalability, and governance across data platforms. Mentoring the team is also required.

Qualifications

• 8+ years of hands-on AI/ML experience and end-to-end implementation of enterprise AI/ML solutions.
• Strong Python development experience and modern software engineering practices.
• Deep knowledge of ML, DL, NLP, and Generative AI/LLMs.
• Experience with RAG, embeddings, vector databases/search, prompt engineering, and LLM evaluation.
• Hands-on cloud platform experience (Azure, AWS, or GCP) and MLOps/LLMOps, model deployment, monitoring, CI/CD, and containers.

Responsibilities

• Architect, design, and implement end-to-end AI/ML and Generative AI solutions.
• Translate business requirements into scalable, secure, production-ready architectures.
• Develop and integrate AI/ML components using Python, APIs, and cloud services.
• Design using LLMs, embeddings, vector search, agentic AI, and model orchestration.
• Operationalize model training, deployment, inference, evaluation, monitoring, and lifecycle management.
• Implement MLOps/LLMOps practices including CI/CD, versioning, testing, observability.

Skills

Python

AI/ML

Machine Learning

NLP

Generative AI

LLMs

MLOps

Cloud Platforms

APIs/Microservices

Tools

CI/CD tooling

Containers

APIs

Job description

Role Summary

We are looking for a SR AI/ML Engineer with strong hands-on experience designing and implementing enterprise AI/ML solutions. This role requires a combination of architecture leadership, machine learning, Generative AI, and cloud expertise.

The ideal candidate will be a hands-on technical contributor who actively works on AI/ML projects end-to-end defining architecture, designing solutions, developing and integrating components, building production-grade implementations, troubleshooting issues, and supporting deployment and operationalization — while also providing technical leadership to the broader engineering team.

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 solutions.
• Actively develop and integrate solutions using Python, AI/ML frameworks, APIs, and cloud services.
• Design and implement solutions using LLMs, RAG, embeddings, vector search, agentic AI, and model orchestration.
• Build and operationalize capabilities for model training, deployment, inference, evaluation, monitoring, and lifecycle management.
• Implement MLOps/LLMOps practices including CI/CD, model and prompt versioning, automated testing, observability, and monitoring.
• Evaluate AI models, frameworks, platforms and technologies and select appropriate solutions based on project requirements.
• Design and implement integrations across AI services, enterprise applications, APIs, data platforms and cloud services.
• Ensure solutions meet enterprise requirements for security, privacy, scalability, performance, reliability, and cost optimization.
• Provide technical leadership through solution design, hands-on implementation, code reviews, troubleshooting, and mentoring.

Required Skills & Experience

• 8+ years of hands-on AI/ML experience, with proven end-to-end implementation experience and deep expertise in architecting, developing, and delivering production-grade AI/ML solutions at enterprise scale.
• Strong hands-on development experience with Python and modern software engineering practices.
• Strong experience across Machine Learning, Deep Learning, NLP, and Generative AI/LLMs.
• Hands-on experience with RAG, embeddings, vector databases/search, prompt and context engineering, and LLM evaluation.
• Strong experience with at least one major cloud platform: Azure, AWS, or GCP.
• Hands-on experience with MLOps/LLMOps, model deployment, monitoring, CI/CD, containers, and APIs/microservices.
• Experience working with structured and unstructured data, data pipelines, and enterprise data platforms.
• Strong understanding of AI security, privacy, Responsible AI, and enterprise governance.
• Strong communication skills with the ability to work effectively across engineering, architecture, product, and business teams.Role & responsibilities