Assistant Manager - SA2 (Ai Hub GTS)

Roles & responsibilities

Here are some of the key responsibilities of Sr Data Scientist:
 

1. Model Development Support: You will support the design, development, and implementation of generative AI models and systems. This involves understanding the problem domain, assisting in selecting appropriate models, helping in training them on large datasets, and fine-tuning hyperparameters under supervision.

2. Algorithm Support: You will assist in optimizing generative AI algorithms to improve their efficiency, scalability, and computational performance. This may involve learning about parallelization, distributed computing, and hardware acceleration techniques.

3. Data Preprocessing and Feature Engineering: You will assist in working with large datasets, preprocess them, and perform feature engineering to extract relevant information for generative AI models. This includes learning about data cleaning, normalization, dimensionality reduction, and feature selection.

4. Model Evaluation and Validation: You will assist in evaluating the performance of generative AI models using appropriate metrics and validation techniques. This involves conducting experiments, analyzing results, and iteratively refining models under the guidance of senior team members.

5. Collaboration and Teamwork: You will collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements, gather feedback, and integrate generative AI models into larger systems or applications.

6. Learning and Development: As an associate, you will focus on learning and developing your skills in generative AI. This includes receiving mentoring, having your work reviewed, and gaining hands-on experience.

7. Documentation and Reporting: You will assist in documenting work, including model architectures, methodologies, and experimental results. You may also be responsible for preparing reports under supervision.

8. Ethical Considerations: You will learn about the ethical implications of generative AI models, such as generating biased or inappropriate content. As an associate, you should be aware of these considerations and ensure that your work adheres to ethical guidelines and principles under the guidance of senior team members. 

Mandatory  technical & functional skills

·In depth knowledge on ML, Deep Learning and NLP algorithms, LLMs ( BERT, GEPT, etc.) and hands-on LangChain, OpenAI LLM Libraries ,  VectorDBs (Chroma, FAISS, etc), ·Hands-on ML platforms  offered through GCP : Vertex AI or  Azure : AI Foundry or AWS SageMaker ·Proficiency in Python, Java, or C++, and machine learning frameworks like TensorFlow or PyTorch developing deep learning projects is crucial. ·Develop and optimize generative AI models, collaborating with cross-functional teams and researching cutting-edge techniques ·Ensure scalability and efficiency, handle data tasks, stay current with AI trends, and contribute to model documentation for internal and external audiences.

Cloud computing experience, particularly with Google/Azure Cloud Platform, is essential. With strong foundation in understating Data Analytics Services offered by Google or Azure ( BigQuery/Synapse)

Preferred technical & functional skills

—Good knowledge on Azure Cognitive Search, Google Cloud Search, AWS Kendra —Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops —Strong oral and written communication skills with the ability to communicate technical and non-technical concepts to peers and stakeholders —Ability to work independently with minimal supervision, and escalate when needed

Key behavioral attributes/requirements

—Ability to mentor junior developers —Ability to own project deliverables, not just individual tasks

Understand business objectives and functions to support data needs

Roles & responsibilities

Here are some of the key responsibilities of Sr Data Scientist:
 

1. Model Development Support: You will support the design, development, and implementation of generative AI models and systems. This involves understanding the problem domain, assisting in selecting appropriate models, helping in training them on large datasets, and fine-tuning hyperparameters under supervision.

2. Algorithm Support: You will assist in optimizing generative AI algorithms to improve their efficiency, scalability, and computational performance. This may involve learning about parallelization, distributed computing, and hardware acceleration techniques.

3. Data Preprocessing and Feature Engineering: You will assist in working with large datasets, preprocess them, and perform feature engineering to extract relevant information for generative AI models. This includes learning about data cleaning, normalization, dimensionality reduction, and feature selection.

4. Model Evaluation and Validation: You will assist in evaluating the performance of generative AI models using appropriate metrics and validation techniques. This involves conducting experiments, analyzing results, and iteratively refining models under the guidance of senior team members.

5. Collaboration and Teamwork: You will collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements, gather feedback, and integrate generative AI models into larger systems or applications.

6. Learning and Development: As an associate, you will focus on learning and developing your skills in generative AI. This includes receiving mentoring, having your work reviewed, and gaining hands-on experience.

7. Documentation and Reporting: You will assist in documenting work, including model architectures, methodologies, and experimental results. You may also be responsible for preparing reports under supervision.

8. Ethical Considerations: You will learn about the ethical implications of generative AI models, such as generating biased or inappropriate content. As an associate, you should be aware of these considerations and ensure that your work adheres to ethical guidelines and principles under the guidance of senior team members. 

Mandatory  technical & functional skills

·In depth knowledge on ML, Deep Learning and NLP algorithms, LLMs ( BERT, GEPT, etc.) and hands-on LangChain, OpenAI LLM Libraries ,  VectorDBs (Chroma, FAISS, etc), ·Hands-on ML platforms  offered through GCP : Vertex AI or  Azure : AI Foundry or AWS SageMaker ·Proficiency in Python, Java, or C++, and machine learning frameworks like TensorFlow or PyTorch developing deep learning projects is crucial. ·Develop and optimize generative AI models, collaborating with cross-functional teams and researching cutting-edge techniques ·Ensure scalability and efficiency, handle data tasks, stay current with AI trends, and contribute to model documentation for internal and external audiences.

Cloud computing experience, particularly with Google/Azure Cloud Platform, is essential. With strong foundation in understating Data Analytics Services offered by Google or Azure ( BigQuery/Synapse)

Preferred technical & functional skills

—Good knowledge on Azure Cognitive Search, Google Cloud Search, AWS Kendra —Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops —Strong oral and written communication skills with the ability to communicate technical and non-technical concepts to peers and stakeholders —Ability to work independently with minimal supervision, and escalate when needed

Key behavioral attributes/requirements

—Ability to mentor junior developers —Ability to own project deliverables, not just individual tasks

Understand business objectives and functions to support data needs

This role is for you if you have  the below

Educational qualifications

-Bachelor’s or Master’s degree in Computer Science

Work experience

6 to 8 Years of Experience