We are looking for an experienced GenAI Engineer to design, build, and scale enterprise-grade Generative AI platforms and applications. The ideal candidate will have deep expertise in LLM engineering, agentic AI, RAG architectures, AI governance, and cloud-native deployments, along with strong hands-on coding skills and the ability to lead engineering teams in delivering production-ready AI solutions.

Key Responsibilities :

- Design and architect enterprise-scale Generative AI platforms and AI-powered business solutions.

- Build and optimize LLM-based applications, RAG pipelines, and Agentic AI workflows using modern orchestration frameworks.

- Develop AI-assisted rule transformation and intelligent automation solutions for enterprise use cases.

- Lead prompt engineering, model evaluation, inference optimization, and LLM integration strategies.

- Design flexible AI architectures supporting proprietary, open-source, and self-hosted foundation models.

- Build scalable AI services, APIs, and microservices for enterprise deployment.

- Develop semantic search, NLP, vector retrieval, and intelligent document processing solutions.

- Implement AI governance, guardrails, observability, explainability, and evaluation frameworks.

- Optimize inference performance, GPU utilization, latency, throughput, and deployment efficiency.

- Lead technical design reviews, mentor engineering teams, and establish best practices for AI development and code quality.

- Collaborate with product, engineering, and business stakeholders to define AI strategy and technical roadmaps.

Required Skills :

- 8 - 13 years of software engineering experience with strong expertise in AI/ML and Generative AI.

- Hands-on experience building enterprise applications using Large Language Models (LLMs).

- Strong expertise in Prompt Engineering, RAG, Vector Databases, and Agentic AI architectures.

- Experience with LangChain, LangGraph, CrewAI, AutoGen, or similar orchestration frameworks.

- Proficiency in Python and modern AI development ecosystems.

- Experience with OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic Claude, or similar AI platforms.

- Knowledge of inference optimization frameworks such as vLLM, SGLang, or equivalent.

- Experience deploying AI applications on AWS, Azure, or Google Cloud Platform using containers and Kubernetes.

- Strong understanding of AI governance, responsible AI, guardrails, model monitoring, and observability.

- Experience building scalable distributed systems, APIs, and cloud-native microservices.

- Excellent problem-solving, leadership, mentoring, and stakeholder management skills.

Preferred Skills :

- Experience with LLM fine-tuning, pre-training, and model optimization techniques.

- Exposure to GPU orchestration, distributed inference, and high-performance AI serving.

- Experience implementing AI solutions in regulated industries.

- Familiarity with MLOps, CI/CD pipelines, model lifecycle management, and AI security.

Education (Mandatory) :

- Full-time B.E./B.Tech, M.Tech, MCA, MS, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related discipline.