Specialist - Research Engineering

Top skills Python Programming Advance Machine Learning Libraries NLP Data

Classification Model Classification Prompt Engineering API RAG LLM Evaluation

Metrics RAGAS or any equivalent Lang Chain Hugging Face CICD

About the Role

We are seeking a dynamic Full Stack AI Assurance Engineer to design develop the test cases with data sets for LLM Evaluation using AI Trust Assure framework and test AILLM evaluations and recommendations The ideal candidate will have a strong foundation in AIML concepts Python programming and RAGAS any equivalent frameworks along with handson experience in prompt engineering and models classification data classification.

Key Responsibilities

Identify and analyse the Data preparations for Model Evaluation specific to AIML systems including ethical legal safety security and operational risks and Evaluate potential for bias unfairness lack of transparency and robustness etc Evaluate and monitor data pipelines to ensure high data quality integrity and representativeness for AIML models. Develop and execute validation plans for AIML models including performance robustness fairness and security testing. Implement tools and practices to enhance the interpretability and transparency of AI models and outcomes.Assess AI systems for ethical and fairness considerations and implement or recommend mitigation strategies as needed. Set up and manage monitoring systems for AI models in production identifying and addressing drifts anomalies or policy violations. Work closely with data scientists ML engineers compliance teams and business stakeholders to embed assurance practices throughout the AI lifecycle . Contribute to the development and continuous improvement of AI governance frameworks and best practices. Perform code reviews debugging and continuous improvement of automation pipelines. Stay updated on emerging trends in AI ML and DL for AI Assurance.

Required Skills

1 Bachelors or Master's degree in Computer Science Data Science Engineering or a related field.

2 5-8 years of experience in AIML data science software quality assurance or a related domain.

3 Hands on Exp in vibe coding such as Copilot Crewai Claude code

4 Strong understanding of AIML concepts model classification and lifecycle

5 Experience with risk management testing validation and monitoring of AI systems

6 Experience with data quality assessment bias detection and fairness evaluation tools.

7 Familiarity with explainable AI XAI techniques and tools

Additional Skills

Experience with Prompt Engineering LLM APIs and chatbot integration

Knowledge of regulatory frameworks and standards for AI eg EU AI Act ISOIEC standards

Knowledge of cloud environments AWS Azure or GCP

Excellent analytical problem solving and documentation skills

Strong analytical debugging and communication skills.