Job Title: Data Scientist - Generative AI & ML Engineering

Location: Pune (WHO)

Experience: 3-5 Years

Role Definition

As a Data Scientist focusing on Generative AI & ML Engineering, you will be responsible for the end-to-end lifecycle of AI solution delivery. Your work will span from initial data exploration and algorithm research to model deployment, monitoring, and integration into existing systems. You'll contribute to an environment that values innovation, robust engineering practices, and practical application of AI to drive impact.

Key Responsibilities:

- Research, design, and develop advanced AI algorithms and models, with a strong focus on Generative AI.

- Analyze large and complex datasets, perform data mining, and ensure data quality and cleanliness.

- Build and deploy predictive models using various Machine Learning (supervised and unsupervised learning) techniques.

- Apply Natural Language Processing (NLP) techniques and leverage Deep Learning techniques, including RNNs and Transformers, to develop sophisticated AI solutions.

- Drive the end-to-end AI solution delivery process, from ideation and prototyping to deployment and integration.

- Develop and implement robust AI integration and deployment strategies for seamless system incorporation.

- Collaborate with engineering teams on MLOps frameworks and model deployment processes to ensure scalability and reliability.

- Monitor data pipelines and deployed models for performance, drift, and retraining needs.

- Conduct Prompt Engineering and implement modern testing methodologies for AI models.

Skills & Qualifications:

Required Skills (Proficient):

- Languages/Frameworks: Strong proficiency in Python and R. Experience with Fast API and Azure UI Search API (React).

- Databases & ETL: Experience with Cosmos DB (API for MongoDB), Azure Data Factory, and Azure Databricks.

- Cloud: Foundational knowledge of Azure Cloud Basics, including Azure DevOps.

- Version Control & Deployment: Experience with GitLab Pipeline, Ansible, and Rex Deployment.

Data Science:

- Solid understanding of Prompt Engineering and modern testing techniques for AI.

- Expertise in data mining and cleaning methodologies.

- Proficiency in Machine Learning (Supervised and Unsupervised Learning).

- Strong knowledge of NLP techniques and Deep Learning techniques, including RNNs and Transformers.

- Experience with end-to-end AI solution delivery, AI integration, and deployment.

- Familiarity with AI frameworks (PyTorch) and MLOps frameworks.

- Understanding of model deployment processes and data pipeline monitoring.

- Advanced Skills (Expert Level - in addition to Proficient skills):

- Generative AI Frameworks: Expertise in Azure Open AI and deep understanding of Open AI GPT Family of models (4o/4/3).

- Vector Search: In-depth knowledge of Embeddings and Vector Search techniques.

- Cloud Storage: Expertise with Azure Storage Account.

- Machine Learning Algorithms: Expert-level proficiency in various machine learning algorithms (supervised, unsupervised, reinforcement learning).

- Deep Learning Frameworks: Proficiency in advanced deep learning frameworks (TensorFlow, PyTorch).

- Mathematical Foundation: Strong mathematical foundation in linear algebra, calculus, probability, and statistics.

- Research & Experimentation: Experience with research methodology and experimental design.

- Data Analysis Tools: Expert proficiency in data analysis tools such as Pandas, NumPy, and SQL.

- Statistical Modeling: Strong statistical and probabilistic modeling skills.

- Data Visualization: Proficient in data visualization tools (e.g., Matplotlib, Seaborn, Tableau).

- Big Data Technologies: Knowledge of big data technologies like Spark and Hive.

- AI-Driven Systems: Experience with AI-driven analytics and decision-making systems.

Qualifications:

- Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Statistics, or a related quantitative field. A Master's or Ph.D. is a plus.

- 3-5 years of hands-on experience as a Data Scientist or Machine Learning Engineer, with a focus on AI/ML development and deployment.

- Demonstrated ability to deliver end-to-end AI solutions in a production environment.

- Strong problem-solving skills and the ability to work in a fast-paced, collaborative team environment.