Coforge - Generative AI Developer - Prompt Engineering (3-6 yrs)

We have an opening for a Gen AI Developer with our team based out at Bengaluru.

About Company :

Coforge is a global digital services and solutions provider, formerly known as NIIT Technologies. Headquartered in Noida, India, and New Jersey, USA, Coforge specializes in leveraging emerging technologies and deep domain expertise to deliver impactful business solutions. We have a strong presence in over 23 countries with 30 global delivery centers.

Coforge has been recognized for its exceptional workplace culture and has received several awards, including being named one of India's Best Workplaces in IT & IT-BPM for 2024.

Work Location : Bangalore (Prestige Shantiniketan, Whitefield)

Exp : 3 to 6 years

Notice Period : Immediate/15 Days

GenAI based skillsets : Prompt Engineering, GenAI Ops

Programming : Python/Java (Python is preferred)

Data Engineering : Feature engineering, SQL (Not purely ETL), To know the Data engineering, Dialogflow CX, Agent Builder, Data Store

Mandatory Skills : Data Analysis

Desirable Skills : Python, Sql, Google Dialogflow, Virtual Agent, Ai & Machine Learning

Hard skills :

- Advanced programming knowledge, including mastery of programming languages such as Python, and especially AI-centric libraries like TensorFlow, PyTorch, and Keras. This includes the ability to implement and manipulate complex algorithms fundamental to developing generative AI models.

- Expertise in generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The candidate should be able to design, train, and optimize these models to generate high-quality, creative content.

- Natural language processing (NLP) for text generation projects. This includes familiarity with techniques for text parsing, sentiment analysis, and the use of transformers like GPT (generative pre-trained transformer) models.

- Data management knowledge, including data pre-processing, augmentation, and generation of synthetic data. This involves cleaning, labeling, and augmenting data to train and improve AI models.

- Cloud computing and deployment knowledge for deploying and managing AI applications on cloud platforms like AWS, Google Cloud, or Microsoft Azure. This includes understanding containerization technologies like Docker and orchestration tools like Kubernetes, which are important for scaling AI solutions.