GenAI Engineer

Job Description
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.

Work arrangement
No