Key Responsibilities

- Provide advanced L3 technical support for AI/GenAI applications, including complex debugging, root cause analysis, and code-level resolution.
- Analyse and enhance Python-based AI applications and backend services.
- Troubleshoot and improve RAG pipelines, including retrieval, prompt engineering, orchestration, embeddings, and response quality.
- Work on LLM integrations such as OpenAI API and AWS Bedrock.
- Develop, debug, and enhance applications using LangChain and LangGraph.
- Work with Agentic AI tools and architectures to improve AI application capabilities.
- Support model training, fine-tuning, evaluation, and optimization activities.
- Diagnose application and infrastructure issues across Azure and Kubernetes/AKS environments.
- Work with Azure AI Search/Cognitive Search and Snowflake Cortex Search for enterprise knowledge retrieval.
- Monitor application and model performance using Datadog, Fiddler, and similar observability tools.
- Collaborate with development, platform, and MLOps teams to implement permanent fixes and improve application reliability.
- Participate in release activities, code enhancements, performance improvements, and production deployments.
- Develop and maintain runbooks, troubleshooting guides, technical documentation, and knowledge-base content.
- Identify recurring issues and implement automation or engineering solutions to reduce incidents.




- Contribute to MLOps and LLMOps practices, including deployment, monitoring, model lifecycle, and operationalization.

Required Skills & Qualifications

- 6+ years of experience in software/AI engineering, application support, or a related technical role.
- Strong hands-on experience with Python development and debugging.
- Solid experience with Generative AI, LLMs, RAG, and enterprise AI applications.
- Solid understanding of MLOps and LLMOps concepts.
- Hands-on experience with LangChain and LangGraph.
- Experience with Agentic AI tools/frameworks and AI orchestration.
- Experience with LLM APIs, preferably OpenAI API and AWS Bedrock.
- Strong understanding of RAG architecture, embeddings, vector search, prompt engineering, and retrieval optimization.
- Experience with model training, fine-tuning, evaluation, and performance optimization.
- Hands-on experience with Microsoft Azure and Kubernetes/AKS.
- Experience with Azure AI Search/Cognitive Search and Snowflake Cortex Search.
- Experience with observability tools such as Datadog and Fiddler.
- Strong troubleshooting, debugging, analytical, and problem-solving skills.
- Ability to understand existing codebases, identify defects, and implement code-level solutions and enhancements.
- Ability to work effectively in environments with limited documentation and independently drive technical solutions.

📌 AI App Support Engineer (Secunderabad)
🏢 Synectics Apac
📍 Secunderabad