GenAI Engineer

We are looking for an experienced GenAI Engineer with strong hands-on expertise in designing, developing, and deploying end-to-end Generative AI solutions. The candidate will be responsible for taking GenAI use cases from problem understanding and solution design through development, integration, testing, deployment, and production support.

Key Responsibilities

• Design and develop end-to-end Generative AI and LLM-based applications.
• Understand business requirements and translate them into practical GenAI solutions.
• Build and implement RAG pipelines, knowledge-based applications, and semantic search solutions.
• Develop AI agents and agentic workflows using LLMs, tools, APIs, and enterprise systems.
• Implement prompt engineering, function/tool calling, structured outputs, memory, and context management.
• Integrate GenAI applications with REST APIs, databases, third-party services, and enterprise applications.
• Work with structured and unstructured data, documents, and enterprise knowledge bases.
• Evaluate and improve LLM applications for accuracy, relevance, latency, reliability, and hallucination.
• Optimize prompts, retrieval pipelines, model selection, and overall application performance.
• Develop scalable and production-ready GenAI applications.
• Deploy, monitor, troubleshoot, and maintain GenAI solutions in production.
• Collaborate with business, product, and engineering teams to identify and implement new GenAI use cases.

Required Technical Skills

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Experience with OpenAI, Azure OpenAI, Anthropic, Gemini, or similar LLM platforms.
• Strong knowledge of RAG, embeddings, vector databases, semantic search, and retrieval pipelines.
• Experience building AI agents / Agentic AI applications.
• Experience with LangChain, LangGraph, or equivalent frameworks.
• Strong Python programming skills.
• Experience with REST APIs, JSON, webhooks, SQL, and database integrations.
• Knowledge of prompt engineering, function calling, tool calling, and structured outputs.
• Experience with LLM evaluation and optimization.
• Strong software engineering, debugging, and problem-solving skills.
• Experience with Git/GitHub and production development practices.

Cloud & Deployment

• Experience with AWS, Azure, or GCP.
• Docker and containerization.
• API/application deployment.
• CI/CD fundamentals.
• Monitoring and observability for production applications.

Preferred Skills

• Multi-agent systems
• MCP and tool-based AI systems
• AI-powered business process automation
• Document AI / OCR
• Conversational AI / Voice AI
• Fine-tuning or model adaptation
• Enterprise GenAI implementations
• SAP, Salesforce, ServiceNow, CRM, or other enterprise integrations
• Kubernetes / cloud-native deployments

Candidate Profile

• 3–5 years of relevant software, AI, or GenAI engineering experience.
• Strong hands-on experience building real-world GenAI applications, not just POCs or prompt-based solutions.
• Ability to independently own the complete GenAI lifecycle:

Business Problem → Solution Architecture → LLM/RAG/Agent Design → Development → Integration → Evaluation → Deployment → Production

• Strong ability to learn and work with rapidly evolving GenAI technologies.
• Candidates with demonstrated experience in building and deploying production GenAI solutions will be preferred.