Generative AI Engineer

We are seeking a hands-on Generative AI Engineer with expertise in designing, developing, and deploying enterprise-scale generative AI and agentic AI solutions. The ideal candidate will have strong AI/ML fundamentals and proven experience building AI copilots, intelligent assistants, RAG solutions, and autonomous agent-based applications that deliver measurable business value.

Responsibilities:

• Design, develop, and deploy production-grade GenAI applications using LLMs and foundation models.
• Build AI copilots, conversational assistants, enterprise search, knowledge management, and automation solutions.
• Develop and optimize Retrieval-Augmented Generation (RAG) architectures using enterprise data sources.
• Design and implement agentic AI solutions, including AI agents, multi-agent systems, workflow orchestration, planning, reasoning, and tool-calling capabilities.
• Develop scalable APIs, microservices, and cloud-native AI applications.
• Implement MLOps, model monitoring, evaluation, governance, and CI/CD pipelines for AI deployments.
• Collaborate with product managers, data scientists, data engineers, and business stakeholders to translate requirements into scalable AI solutions.
• Drive continuous improvement of AI systems for performance, accuracy, reliability, security, and cost optimization.

Requirements:

• Bachelor's or master's degree in computer science, AI, data science, engineering, or a related field.
• 7-8 years of overall experience, with at least 3-4 years in generative AI and agentic AI.
• Proven experience deploying enterprise AI solutions from concept through production at scale.
• Strong understanding of software engineering, cloud-native architectures, and AI governance.
• A highly skilled GenAI professional who combines deep expertise in LLMs, RAG, agentic AI, multi-agent systems, and cloud AI platforms with strong software engineering capabilities to build and scale enterprise AI solutions across manufacturing, renewable energy, power, and corporate functions.

Generative AI and Agentic AI:

• LLMs (GPT, Claude, Gemini, Llama, Mistral, and others).
• Prompt Engineering.
• RAG Architecture.
• Fine-Tuning and Model Evaluation.
• AI Agents and Multi-Agent Systems.
• LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI.
• Knowledge Graphs and Enterprise Search.

Programming and AI Engineering:

• Python (Mandatory), SQL.
• FastAPI, REST APIs, Microservices.
• TensorFlow, PyTorch, Hugging Face.
• Vector Databases (Azure AI Search, Pinecone, ChromaDB, FAISS).

Cloud and MLOps:

• Azure OpenAI, Azure AI Foundry, Azure ML/AWS Bedrock/SageMaker, or Google Vertex AI/Gemini Enterprise.
• Docker, Kubernetes.
• GitHub Actions / CI/CD.