Senior AI Engineer

Dugar Housing; Chennai District; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

Dugar Housing in Chennai seeks a Senior AI Engineer to design, develop, deploy, and optimize AI-powered solutions for business needs. You will guide architecture decisions, build production-grade ML services, and mentor junior engineers across the AI lifecycle.

You will work with product, software, data, and security teams to implement scalable, secure AI systems on cloud-native platforms, leveraging RAG, embeddings, vector databases and CI/CD practices.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, AI, or related field; Masters preferred.
• 5+ years of professional software engineering or AI/ML engineering experience.
• 3+ years delivering AI/ML/NLP/ GenAI in production.
• Experience designing AI systems for enterprise platforms.
• Experience collaborating with cross-functional teams in agile environments.

Responsibilities

• Design, build, and deploy production-ready AI/ML/GenAI solutions.
• Architect end-to-end AI systems with data ingestion, modeling, and inference layers.
• Develop RAG-based applications with embeddings and vector databases.
• Implement MLOps and LLMOps, CI/CD, model versioning, monitoring, and drift detection.
• Mentor engineers and document architectures and runbooks.
• Evaluate third-party AI services and ensure secure, compliant solutions.

Skills

Python

PyTorch

TensorFlow

LLM Platforms

Cloud AI

Docker

Kubernetes

Data pipelines

Observability

Security

Education

Bachelor's in Computer Science

Master's degree preferred

Tools

Azure OpenAI

OpenAI APIs

Hugging Face

LangChain

LangGraph

Semantic Kernel

Job description

Job Profile: Senior AI Engineer

Role Summary

The Senior AI Engineer is responsible for designing, developing, deploying, and continuously improving AI-powered solutions that solve real business problems. This role combines hands-on engineering expertise with strong architecture knowledge, including cloud-native design, AI system architecture, data pipelines, model deployment, observability, security, and scalability. The ideal candidate can take AI initiatives from concept to production while guiding technical decisions and mentoring engineering teams.

Key Responsibilities

• Design, build, and deploy production-ready AI, machine learning, and Generative AI solutions.
• Architect end-to-end AI systems, including data ingestion, model selection, inference services, integration layers, monitoring, and feedback loops.
• Develop LLM-based applications using prompt engineering, Retrieval-Augmented Generation, vector databases, function calling, agentic workflows, and evaluation frameworks.
• Implement MLOps and LLMOps practices such as CI/CD pipelines, model versioning, model registry, automated testing, deployment automation, monitoring, and drift detection.
• Collaborate with product managers, software engineers, data engineers, solution architects, security teams, and business stakeholders to translate requirements into scalable AI solutions.
• Evaluate third-party AI services, open-source models, cloud AI platforms, and internal build-versus-buy options.
• Ensure AI solutions follow responsible AI principles, including privacy, security, fairness, explainability, auditability, and compliance.
• Optimize AI solutions for performance, cost, latency, reliability, and maintainability.
• Mentor junior and mid-level engineers and promote engineering best practices across the AI development lifecycle.
• Create and maintain technical documentation, solution designs, architecture diagrams, model cards, runbooks, and operational guidelines.
• Apply RAG principles across solution design, including grounding, retrieval quality, source traceability, context relevance, evaluation, and continuous improvement.
• Strong understanding of solution architecture principles, including scalability, availability, reliability, security, maintainability, and integration patterns.
• Ability to design AI reference architectures for cloud, hybrid, and enterprise environments.
• Experience with microservices, APIs, event-driven architecture, containerization, Kubernetes, serverless services, and distributed systems.
• Knowledge of data architecture, including data lakes, data warehouses, streaming pipelines, feature stores, metadata management, and data governance.
• Ability to design secure AI systems with role-based access control, secrets management, encryption, audit logs, and secure data handling.
• Understanding of enterprise architecture alignment, architecture review processes, non-functional requirements, and technical risk management.
• Ability to communicate architecture decisions clearly through diagrams, technical design documents, and stakeholder presentations.

Required Technical Skills

• Strong programming skills in Python and experience building production-grade services.
• Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, or equivalent tools.
• Experience with LLM platforms and tooling such as Azure OpenAI, OpenAI APIs, Hugging Face, LangChain, LangGraph, Semantic Kernel, or similar frameworks.
• Experience designing RAG solutions using embeddings, chunking strategies, retrieval logic, reranking, grounding, and vector databases.
• Strong knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud, including AI/ML services and deployment services.
• Experience with Docker, Kubernetes, CI/CD, Git, automated testing, infrastructure as code, and monitoring tools.
• Knowledge of databases including SQL, NoSQL, search indexes, and vector databases.
• Ability to evaluate AI model quality using metrics, benchmark datasets, regression tests, human review workflows, and safety checks.
• Understanding of software engineering fundamentals including clean code, design patterns, API design, logging, observability, and performance tuning.
• Strong understanding of solution architecture principles, including scalability, availability, reliability, security, maintainability, and integration patterns.
• Ability to design AI reference architectures for cloud, hybrid, and enterprise environments.
• Experience with microservices, APIs, event-driven architecture, containerization, Kubernetes, serverless services, and distributed systems.
• Knowledge of data architecture, including data lakes, data warehouses, streaming pipelines, feature stores, metadata management, and data governance.
• Ability to design secure AI systems with role-based access control, secrets management, encryption, audit logs, and secure data handling.
• Understanding of enterprise architecture alignment, architecture review processes, non-functional requirements, and technical risk management.
• Ability to communicate architecture decisions clearly through diagrams, technical design documents, and stakeholder presentations.

Qualifications and Experience

• Bachelors degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field; Masters degree preferred.
• 5+ years of professional software engineering or AI/ML engineering experience.
• 3+ years of hands-on experience delivering AI, ML, NLP, GenAI, or data-driven applications into production.
• Proven experience designing AI systems or contributing to solution architecture for enterprise-grade platforms.
• Experience working with cross-functional teams in agile delivery environments.
• Strong communication skills with the ability to explain complex AI and architecture concepts to both technical and non-technical stakeholders.