Hiring For Gen AI Developer

Glauben Technologies; Hyderabad, Chennai District, Bengaluru; Hybrid; INR 3,600,000 - 6,000,000; Full time

Job summary

Glauben Technologies in Hyderabad, India seeks a Generative AI Technical Lead with hands-on GenAI expertise to drive development, prompt engineering, and end-to-end AI workflow design. You will mentor a team, design data pipelines, and ensure production-grade quality for AI models and deployments.

The role requires deep knowledge of GANs/VAEs, NLP transformers, and responsible AI practices, with experience across cloud platforms and modern ML tools.

Qualifications

• More than 5 years IT industry experience including 2/3 years in Generative AI/GenAI.

Responsibilities

• Lead the development, implementation, and optimization of Generative AI solutions.

Skills

Generative AI

Prompt engineering

LLM pipelines

ML algorithms

NLP

Computer vision

Responsible AI

Data privacy

Workflow optimization

Team leadership

Software development

CI/CD

Git

Tools

Langchain

Semantic Kernels

Function calling

TensorFlow

Keras

AWS

Azure

GCP

Open source LLMs

Job description

Role & responsibilities

No of years Experience: More than 5 Years of IT industry experience in which 2/3 years should be in

AI/ML/DS domain, including Gen AI technologies.

Job Summary: We are seeking an accomplished Generative AI Technical Lead to spearhead the

development, implementation, and optimization of Generative AI solutions. As the Lead Developer, you

will play a pivotal role in prompt engineering, pipeline creation, workflow establishment, and ensuring

the quality of the technical outputs generated by the team. This role requires strong technical expertise

in Generative AI, hands- on experience in development, and the ability to lead and guide a team

effectively.

Primary Skill Set

• Generative AI Expertise: Good understanding of various Generative AI techniques, including GANs, VAEs, and other relevant architectures. Proven experience in applying these techniques to real-world problems for tasks such as image and text generation. Conversant with Gen AI development tools like Prompt engineering, Langchain, Semantic Kernels, Function calling. Exposure to both API based and opens source LLMs based solution design.


• Technical Proficiency:
  
  
  
  • Machine learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks
  
  
  • Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras
  
  
  • Cloud computing platforms: AWS, Azure, GCP
  
  
  • Natural language processing (NLP): Transformer models, attention mechanisms, word embeddings
  
  
  • Computer vision: Convolutional neural networks, recurrent neural networks, object detection
  
  
  • Robotics: Reinforcement learning, motion planning, control systems
  
  
  • Data ethics: Bias in machine learning, fairness in algorithms
  
  
  • Responsible AI: Should have proficient knowledge in Responsible AI and Data Privacy principles to ensure ethical data handling, transparency, and accountability in all stages of AI development. Must demonstrate a commitment to upholding privacy standards, mitigating bias, and fostering trust within data-driven initiatives.
  
  
  • LLM Pipeline Creation: Strong experience in designing data pipelines, including data preprocessing, feature extraction, and model integration. Familiarity with best practices for creating efficient and scalable pipelines.
  
  
  
  


• Software Development: Proficiency in software development practices, version control systems (e.g., Git), and collaborative coding environments. Understanding of agile methodologies is advantageous.


• Testing and Deployment: Familiarity with testing methodologies for AI models, including unit testing, integration testing, and model validation. Experience in deploying models to production environments.


• Workflow Optimization: Knowledge of workflow optimization techniques and tools to enhance development speed and efficiency. Understanding of CI/CD (Continuous Integration/Continuous Deployment) principles.

Secondary Skill Set

• Software Development: Proficiency in software development practices, version control systems (e.g., Git), and collaborative coding environments. Understanding of agile methodologies is advantageous.


• Testing and Deployment: Familiarity with testing methodologies for AI models, including unit testing, integration testing, and model validation. Experience in deploying models to production environments.


• Workflow Optimization: Knowledge of workflow optimization techniques and tools to enhance development speed and efficiency. Understanding of CI/CD (Continuous Integration/Continuous Deployment) principles.

Roles & Responsibilities

• Technical Leadership: Lead a team of developers in the creation, implementation, and optimization of Generative AI solutions. Provide technical guidance, resolve challenges, and foster a collaborative environment.


• Prompt Engineering: Spearhead the design and development of prompt engineering strategies to influence and control the output of Generative AI models. Optimize prompts for desired results.


• Pipeline Design: Design end-to-end data pipelines that encompass data preprocessing, feature engineering, model training, and deployment. Ensure pipelines are efficient, scalable, and well-documented.


• Technical Review: Review the technical outputs generated by the team, including code, models, and pipelines. Ensure high-quality and maintainable solutions that adhere to best practices.


• Testing and Validation: Implement testing methodologies to validate the performance and accuracy of Generative AI models. Develop and execute unit tests, integration tests, and validation strategies.


• Deployment Strategy: Collaborate with DevOps and deployment teams to deploy trained models into production environments. Ensure smooth integration and monitor performance post-deployment.


• Workflow Optimization: Identify opportunities to optimize development workflows, enhance productivity, and streamline processes. Implement tools and practices to improve efficiency.


• Collaboration: Interface with cross-functional teams, including data scientists, architects, and business stakeholders. Collaborate on solution design, implementation, and project milestones.


• Documentation: Maintain comprehensive documentation of technical designs, code, and workflows. Ensure documentation is up-to-date, accessible, and understandable for team