AI / ML Engineer

The ideal candidate should have hands-on expertise in building scalable backend systems using. NET technologies and experience working with modern cloud environments such as Azure or GCP.

Responsibilities

• Build LLM-powered features (assistants/chat, copilots, automated workflows) and integrate them into products via APIs/services.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines: document ingestion, chunking, embeddings, indexing, retrieval, re-ranking, and grounding/citation patterns.
• Apply prompt engineering and prompt management: templates, guardrails, structured outputs (e. g., JSON), and iterative improvement.
• Create evaluation and quality frameworks: test sets, rubrics, automated checks, regression testing, and monitoring of AI output quality.
• Apply ML fundamentals to improve AI system performance: select appropriate metrics (accuracy/precision/recall/F1 where relevant), analyse failure modes, and run experiments.
• Implement production-ready engineering practices: logging, tracing, error handling, performance tuning, cost optimisation, and secure data handling.
• Collaborate with data engineering/analytics on data extraction, transformations, validation checks, and pipeline reliability.
• Document system design, experiments, and operational runbooks; contribute to best practices and reusable components.
• Responsible for designing and developing resiliency in the infrastructure, troubleshooting incidents, engaging with squads to address failure patterns, and participating in incident management.
• Interest in working on. NET projects when required.

Requirements

• 3+ years of experience in AI Engineering / ML Engineering / Software Engineering with applied AI delivery.
• Strong Python proficiency (clean, testable, maintainable code; debugging; performance awareness).
• Hands-on experience with LLMs / GenAI: prompts, embeddings, orchestration frameworks, and building real features (not just demos).
• Experience building and tuning RAG systems (retrieval quality, chunking strategies, hybrid search, reranking/grounding).
• ML exposure (core concepts): supervised learning basics, train/validation/test, evaluation metrics, bias/variance intuition, and error analysis.
• Working knowledge of data engineering/analysis: SQL fundamentals, data profiling, ETL/ELT concepts, and data quality checks.
• Familiarity with deploying services in production (REST APIs, containers, CI/CD basics, and monitoring/observability).

This job was posted by Vriddhi Seth from CodeVyasa.