Senior Associate 2 PD (AI Engineer) Full-time

Job Description

Roles & responsibilities

Here are some of the key responsibilities of Generative AI Engineer :

1. Model Development and Implementation: You will design, develop, and implement generative AI models and systems. This involves understanding the problem domain, selecting appropriate models, training them on large datasets, fine-tuning hyperparameters, and optimizing performance.

2. Algorithm Optimization: You will optimize generative AI algorithms to improve their efficiency, scalability, and computational performance. This may involve parallelization, distributed computing, and hardware acceleration techniques to leverage the full potential of modern computing architectures.

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

4. Model Evaluation and Validation: You will evaluate the performance of generative AI models using appropriate metrics and validation techniques. This involves conducting experiments, analyzing results, and iteratively refining models to achieve desired outcomes.

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. Technical Leadership: As a senior engineer, you will provide technical leadership and guidance to junior team members. This includes mentoring, reviewing their work, and helping them develop their skills in generative AI.

7. Documentation and Reporting: You will document your work, including model architectures, methodologies, and experimental results. You may also be responsible for preparing technical reports, presentations, and whitepapers to communicate findings and insights to stakeholders.

8. Continuous Learning and Innovation: You should stay updated with the latest research papers, attend conferences, and participate in relevant communities to continuously learn and innovate in the field of generative AI. You should also encourage a culture of learning and innovation within your team.

9. Ethical Considerations: Generative AI models can have ethical implications, such as generating biased or inappropriate content. As a senior engineer, you should be aware of these considerations and ensure that your models adhere to ethical guidelines and principles.

Mandatory technical & functional skills
• Proficiency in Python or R, and machine learning frameworks like TensorFlow or PyTorch
• In depth knowledge on ML and NLP algorithms, LLMs ( BERT, GEPT, etc.) and hands-on LangChain (LangGraph , LangSmith) or LlamaParse/LlamaCloud or Semantic Kernel, OpenAI LLM Libraries, VectorDBs (Chroma DB, FAISS, etc.)
• Cloud computing experience, particularly with Azure Cloud Platform is essential.
• Productionized experience with LLM application using RAG pipeline
• 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.

Strong oral and written communication skills with the ability to communicate technical and non-technical concepts to peers and stakeholders

Preferred technical & functional skills
• Hands-on ML platforms offered through GCP : Vertex AI or Azure : ML Studio
• 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

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

Responsibilities

Roles & responsibilities

Here are some of the key responsibilities of Generative AI Engineer :

1. Model Development and Implementation: You will design, develop, and implement generative AI models and systems. This involves understanding the problem domain, selecting appropriate models, training them on large datasets, fine-tuning hyperparameters, and optimizing performance.

2. Algorithm Optimization: You will optimize generative AI algorithms to improve their efficiency, scalability, and computational performance. This may involve parallelization, distributed computing, and hardware acceleration techniques to leverage the full potential of modern computing architectures.

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

4. Model Evaluation and Validation: You will evaluate the performance of generative AI models using appropriate metrics and validation techniques. This involves conducting experiments, analyzing results, and iteratively refining models to achieve desired outcomes.

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. Technical Leadership: As a senior engineer, you will provide technical leadership and guidance to junior team members. This includes mentoring, reviewing their work, and helping them develop their skills in generative AI.

7. Documentation and Reporting: You will document your work, including model architectures, methodologies, and experimental results. You may also be responsible for preparing technical reports, presentations, and whitepapers to communicate findings and insights to stakeholders.

8. Continuous Learning and Innovation: You should stay updated with the latest research papers, attend conferences, and participate in relevant communities to continuously learn and innovate in the field of generative AI. You should also encourage a culture of learning and innovation within your team.

9. Ethical Considerations: Generative AI models can have ethical implications, such as generating biased or inappropriate content. As a senior engineer, you should be aware of these considerations and ensure that your models adhere to ethical guidelines and principles.

Mandatory technical & functional skills
• Proficiency in Python or R, and machine learning frameworks like TensorFlow or PyTorch
• In depth knowledge on ML and NLP algorithms, LLMs ( BERT, GEPT, etc.) and hands-on LangChain (LangGraph , LangSmith) or LlamaParse/LlamaCloud or Semantic Kernel, OpenAI LLM Libraries, VectorDBs (Chroma DB, FAISS, etc.)
• Cloud computing experience, particularly with Azure Cloud Platform is essential.
• Productionized experience with LLM application using RAG pipeline
• 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.

Strong oral and written communication skills with the ability to communicate technical and non-technical concepts to peers and stakeholders

Preferred technical & functional skills
• Hands-on ML platforms offered through GCP : Vertex AI or Azure : ML Studio
• 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

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

Qualifications

This role is for you if you have the below

Educational qualifications

-Bachelor’s degree in Computer Science

Work experience
• 4+ Years of AI/ML experience