We are seeking a Senior GenAI / Agentic AI Engineer to lead the design and implementation of production-grade AI solutions for complex enterprise use cases.

The candidate will own AI application architecture, agentic workflows, RAG systems, model integrations, and production engineering.

This role requires a combination of strong hands-on engineering capability and technical ownership.

The candidate will work closely with architects, product teams, data teams, security teams, and engineering stakeholders to build scalable AI solutions.

Key Responsibilities :

AI Solution Design :

- Translate business requirements into scalable GenAI and Agentic AI solutions.
- Design production architectures for LLM-powered applications.
- Select appropriate models, frameworks, retrieval strategies, and deployment approaches.
- Define reusable patterns for enterprise AI application development.
- Evaluate technical trade-offs between different AI architectures and technologies.

Agentic AI Architecture :

- Design multi-agent and multi-step agent workflows for complex enterprise processes.
- Implement tool-calling architectures and AI workflows integrating multiple enterprise systems.
- Design agent memory, context management, orchestration, and state-handling mechanisms.
- Establish controls for agent reliability, failure handling, and deterministic workflow execution.
- Build reusable agent frameworks and components.

RAG & Knowledge Systems :

- Design scalable RAG architectures for enterprise knowledge systems.
- Optimize document ingestion, chunking, embeddings, retrieval, reranking, and context construction.
- Work with vector databases and hybrid search technologies.




- Define strategies for improving retrieval accuracy and response quality.
- Design evaluation frameworks for RAG and LLM-based applications.

LLM Engineering :

- Integrate and evaluate multiple LLM providers and models.
- Design prompt engineering and structured-output strategies.
- Implement model routing, fallback mechanisms, and inference optimization.
- Work with Azure OpenAI and other enterprise model platforms.
- Contribute to model evaluation, fine-tuning, quantization, or optimization initiatives where appropriate.

Production Engineering :

- Design scalable APIs and microservices for AI applications.
- Deploy AI workloads using Docker and Kubernetes.
- Establish CI/CD pipelines for AI application delivery.
- Implement monitoring, logging, tracing, and AI-specific observability.
- Define performance, availability, and scalability requirements.
- Troubleshoot complex production issues and conduct root-cause analysis.

Security & Responsible AI :

- Design security controls for enterprise AI applications.
- Implement guardrails, content controls, access controls, and data protection mechanisms.
- Establish practices for prompt security and protection against common LLM application risks.
- Support Responsible AI, compliance,



and governance requirements.

Technical Leadership :

- Lead technical design discussions and architecture reviews.
- Mentor engineers working on GenAI and Agentic AI solutions.
- Conduct code and design reviews.
- Establish development standards and reusable engineering practices.
- Collaborate with product, architecture, security, and data teams.

Required Skills :

- Extensive hands-on GenAI development experience.
- Strong Agentic AI and multi-step workflow experience.
- Python and/or JavaScript/TypeScript.
- LLM application architecture.
- RAG architecture.
- Vector databases and semantic search.
- Azure OpenAI or equivalent enterprise AI platforms.
- LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks.
- API and microservices architecture.
- Docker and Kubernetes.
- CI/CD and cloud deployment.
- AI application observability and evaluation.

Positive to Have :

- Multi-agent frameworks.
- Model fine-tuning and quantization.
- Ragas, TruLens, DeepEval, or equivalent evaluation frameworks.
- Event-driven AI systems.
- Real-time inference architectures.
- MLOps / LLMOps.
- Experience with AWS or GCP in addition to Azure.

Candidate Profile :

- Strong combination of hands-on AI engineering and technical leadership.
- Experience taking GenAI solutions from architecture through production.
- Strong understanding of distributed systems and cloud-native engineering.
- Ability to own complex AI initiatives independently.
- Strong stakeholder management and communication skills.
- Ability to mentor engineers and influence technical decisions

📌 Infosys - Senior Generative AI/Agentic AI Engineer (India)
🏢 EdgeVerve Systems
📍 India