About the Role :

We are looking for a hands-on AI Engineer to design, develop and deploy next-generation Agentic AI and Generative AI solutions at Incedo. The role involves building production-grade LLM applications, RAG pipelines, AI agents and intelligent automation solutions.

What You'll Own :

1. Agentic AI & GenAI Engineering :

- Design, develop and deploy production-ready GenAI applications, RAG pipelines and agentic workflows.

- Build AI agents using multi-agent orchestration, tool calling, memory and enterprise integrations.

- Work with frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen or LlamaIndex.

- Implement MCP-based integrations and connect LLMs with enterprise systems, APIs and data sources.

- Develop and optimize retrieval strategies, embeddings, vector databases and contextual search.

- Evaluate foundation models and select appropriate models based on performance, accuracy, cost and latency.

2. AI Evaluation, Quality & Governance :

- Develop evaluation frameworks for LLM applications, including accuracy, relevance and hallucination assessments.

- Implement prompt engineering, prompt versioning and regression testing.

- Establish guardrails for responsible AI, PII protection and secure prompting.

- Monitor model performance, latency, token consumption and application reliability.

- Troubleshoot and optimize RAG pipelines and agentic workflows.

3. Platform Engineering & Delivery :

- Build and deploy AI solutions on cloud platforms such as Azure, AWS or GCP.

- Develop scalable APIs and microservices using Python and related frameworks.

- Containerize applications using Docker and deploy using Kubernetes.

- Integrate AI applications into CI/CD pipelines and production environments.

- Collaborate with product managers, architects and engineering teams to deliver AI capabilities.

4. Technical Collaboration :

- Drive technical implementation and contribute to architecture and design discussions.

- Conduct code reviews and follow engineering best practices.

- Mentor junior engineers and contribute to technical knowledge sharing.

- Identify opportunities to improve reusable AI components and engineering practices.

What You'll Bring :

- 4 - 8 years of experience in software engineering, AI/ML or related engineering domains.

- At least 2 - 4 years of hands-on experience building AI/ML or GenAI solutions.

- Strong programming skills in Python and experience developing production-grade applications.

- Hands-on experience building LLM-based applications and RAG solutions.

- Experience with agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen or LlamaIndex.

- Strong understanding of embeddings, vector databases, semantic search and retrieval optimization.

- Experience with Azure OpenAI, Azure AI, AWS Bedrock or Google Vertex AI.

- Familiarity with Docker, Kubernetes, REST APIs and CI/CD pipelines.

- Understanding of LLM evaluation, observability, prompt engineering and AI safety practices.

- Strong problem-solving, debugging and communication skills.