Role: Builds and iterates on GenAI-powered pipelines that automate the conversion of legacy code and SQL constructs into modern, cloud-compatible equivalents. Responsible for prompt engineering, LLM integration, and Python-based tooling that orchestrates, validates, and quality-checks AI-generated outputs before handoff to engineering teams. Works closely with Data Architects and Data Modellers to understand source patterns and target standards, continuously refining pipelines to improve conversion accuracy and reduce manual rework.
Experience: 5–7 years in software or data engineering with a strong Python background, including at least 2 years working with LLMs, GenAI frameworks, or AI-assisted automation pipelines. Prior exposure to SQL-heavy environments or code modernization projects is a robust advantage.
Skills:
LLM Integration & Prompt Engineering: Designing and iterating on prompts for code conversion tasks using models such as Claude, GPT-4, or equivalent, including few-shot prompting, chain-of-thought reasoning,



and output formatting to maximize accuracy
Python Development: Building production-quality pipelines for orchestrating LLM API calls, parsing and validating AI-generated code, and automating batch conversion workflows
SQL & Data Warehouse Awareness: Working knowledge of legacy and modern SQL dialects sufficient to evaluate whether AI-generated conversions are semantically correct
Output Validation: Building automated checks that compare AI-generated code against expected patterns, flag low-confidence outputs, and route complex cases for human review
GenAI Frameworks: Hands-on experience with LangChain, LlamaIndex, or similar; familiarity with embedding models and vector stores is an advantage
Version Control & MLOps: Git-based prompt and pipeline versioning; familiarity with experiment tracking to benchmark prompt iterations against conversion quality metrics

📌 AI Engineer (Chennai)
🏢 Movate
📍 Chennai