GenAI Solution Architect/Lead

About the Company :

Headquartered in California, U.S.A., GSPANN provides consulting and IT services to global clients. We help clients transform how they deliver business value by helping them optimize their IT capabilities, practices, and operations with our experience in retail, high-technology, and manufacturing. With five global delivery centers and 1900+ employees, we provide the intimacy of a boutique consultancy with the capabilities of a large IT services firm.

Role: GenAI Solution Architect/Lead

Experience: 6+ Yrs

Location: Pune

Job Position (Title) GenAI Solution Architect/Lead

Technical Skill Requirements :LLMs, Python, Cloud, Azure AI/Azure ML, Chatbot, MLOps, LLMOps, Data Science

Responsibilities

We are seeking a highly skilled and experienced Lead Data Scientist with a strong background in machine learning, computer vision, natural language processing (NLP), generative AI, LLM and cloud technologies. As the Lead Data Scientist, you will be responsible for leading our data science team on for the client and driving the design, architecture, development and implementation of Generative AI based applications. You will be responsible for providing solutions by continuously keeping up with the new developments in this space. You will work closely with cross-functional teams, including engineers, researchers, and product managers, to design and deploy cutting-edge GenAI solutions that address our business needs. The ideal candidate will have a minimum of 5 years of real hands on experience in machine learning/LLMs with expert level python coding skills, with a proven track record of successfully delivering projects in computer vision, NLP, generative AI, and at least one cloud platform.

Responsibilities:

• Drive the end-to-end development and deployment of GenAI based applications, instrumenting model tuning and evaluation and managing model drifts leveraging cloud-based infrastructure and services.
• Collaborate with cross-functional teams to understand business requirements and translate them into data science projects that deliver tangible value.
• Stay up-to-date with the latest advancements in Chat/voice bot, NLP, generative AI, and cloud technologies, and apply the latest research findings to enhance existing models and algorithms.
• Evaluate and select appropriate cloud-based tools, frameworks, and services to support data science initiatives, ensuring scalability, reliability, and cost-efficiency.
• Collaborate with cloud engineering teams to optimize the deployment and management of machine learning models in cloud environments.
• Communicate complex technical concepts and findings to both technical and non-technical stakeholders through clear and concise presentations and reports.

Required Skills :

• Bachelor's or master's degree in computer science, data science, or a related field.
• Minimum of 5 years of experience in machine learning, with a focus on chatbot, NLP, generative AI, and cloud technologies.
• Strong expertise in programming languages such as Python, as well as proficiency in relevant libraries and frameworks.
• Proven experience in developing and implementing LLM based applications.
• Deep understanding of GenAI techniques, such as RAGs, Agent base architecture, Prompt Engineering etc.
• Familiarity with generative AI models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs).
• Solid knowledge of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers is a plus.
• Experience working with large-scale datasets and data pre-processing techniques.
• Strong analytical and problem-solving skills, with the ability to think critically and creatively to solve complex challenges.
• Experience with at least one cloud platforms such as AWS, Azure, or Google Cloud, including knowledge of cloud-based machine learning services and infrastructure. Azure preferable.
• Excellent communication and interpersonal skills, with the ability to collaborate effectively with both technical and non-technical stakeholders.