USI | Audit Services | AI and Data Science Engineer III
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro Bangalore location, and mid-level ML role (5-6yrs) drive high applicant competition.
Core ML, LLM, and cloud skills are transferable, but audit-specific context raises domain sensitivity to medium.
Explicit 5-6 years plus mandatory ML/LLM, cloud, and DevOps tooling requirements create high shortlisting strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Lead design, development, and deployment of machine learning models within assigned data science workstreams, focusing on feature engineering and model performance.
Develop high-quality, production-ready code with well-designed APIs, collaborating with Data Science Managers and subject matter experts to solve complex problems and refine solutions.
Contribute to project planning and prioritization, supervise junior team members, and stay updated with latest data science trends for potential application.
Minimum Requirements
5-6 years of industry experience in designing, developing, and deploying machine learning models.
Undergraduate degree in a quantitative field such as computer science, engineering, mathematics, physics, machine learning, or statistics.
Proficiency in Python with relevant libraries (NumPy, Pandas, Scikit-learn), cloud ecosystems (Azure, GCP, AWS), machine learning frameworks (TensorFlow, PyTorch, OpenAI, LangChain) and DevOps tools (Docker, Jenkins, Kubernetes).
Understanding of LLMs, prompt engineering, and core competencies like NLP, generative AI, anomaly detection, time series, or knowledge graphs.
Ideal Candidate Profile
Experienced in applying advanced AI/ML techniques including generative AI, prompt engineering, and large language models within audit or assurance contexts.
Capable of writing clean, well-tested, documented production code and leading technical problem-solving collaboratively with teams.
Familiar with cloud ML pipelines (especially Azure ML), Agile workflows, and deployment automation, with demonstrated mentoring experience and a strategic outlook on project direction.