Hiring For Gen AI Developer

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.

• Leadership Skills: Proven leadership capabilities to guide and mentor a team of developers.

Ability to provide technical direction, solve challenges, and inspire innovation within the team.

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