Staff Engineer - Artificial Intelligence/Machine Learning - Automotive Domain

Key Responsibilities:

- Design, develop, and deploy AI/ML models to solve complex business problems in the Automotive domain.

- Build and optimize machine learning models using supervised and unsupervised learning techniques.

- Perform data preprocessing, feature engineering, model training, evaluation, and hyperparameter tuning.

- Develop AI solutions using Python and libraries such as Pandas, NumPy, and Scikit-learn.

- Design and implement LLM-powered applications with appropriate LLM guardrails to ensure secure, reliable, and responsible AI outputs.

- Analyze large datasets using Advanced SQL and derive actionable business insights.

- Collaborate with business stakeholders, product teams, and engineering teams to gather requirements and translate them into AI solutions.

- Monitor model performance and continuously improve model accuracy and scalability.

- Prepare technical documentation, reports, and effectively communicate findings to technical and business stakeholders.

Required Skills & Experience:

- 5-10 years of experience in Data Science, AI, and Machine Learning.

- Strong hands-on experience with:

1. Python

2. Machine Learning

3. Advanced SQL

4. Data Science

5. LLM Guardrails

- Proficiency in Python libraries:

1. Pandas

2. NumPy

3. Scikit-learn

- Strong understanding of:

1. Supervised Learning

2. Unsupervised Learning

3. Model Evaluation

4. Hyperparameter Tuning

- Experience designing and deploying production-grade AI/ML solutions.

- Strong analytical, problem-solving, and communication skills.

- Experience collaborating with cross-functional teams in Agile environments.

Preferred Skills:

- Experience in the Automotive domain.

- Knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG).

- Experience with cloud platforms such as Azure, AWS, or GCP.

- Familiarity with data visualization tools such as Power BI, Tableau, or Matplotlib.

- Exposure to MLOps and model deployment pipelines.

Educational Qualification:

- Master's degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative discipline.

- Certifications such as Certified Data Scientist or Google Professional Machine Learning Engineer are an added advantage.

Key Competencies:

- AI & Machine Learning

- Data Science & Predictive Analytics

- Python (Pandas, NumPy, Scikit-learn)

- Advanced SQL

- LLMs & LLM Guardrails

- Model Development & Optimization

- Automotive Domain Analytics

- Stakeholder Management & Cross-functional Collaboration

(ref:hirist.tech)