Senior Data Scientist

At Converge Technology Solutions, we're at the forefront of cutting-edge technology and innovation. We are seeking a highly skilled and experienced Generative AI Data Scientist & Technical Consultant to join our team and play a pivotal role in helping our customers harness the power of Generative AI services. In this multifaceted role, you will serve as both a subject matter expert and a technical consultant, taking a lead in pre-sales calls, as well as delivering innovative solutions to our valued clients.

Key Responsibilities:

1. Customer Advisor:

   - Collaborate with our clients to design and implement state-of-the-art Generative AI solutions.

   - Develop prototypes, proof of concepts, and explore novel solutions tailored to their specific needs.

   - Work closely with customers and engage with the academic community to push the boundaries of AI.

2. Thought Leadership:

   - Champion our Generative AI services, sharing best practices and expertise through publications, industry events, and public speaking engagements.

3. Collaboration and Support:

   - Partner with Solutions Architects, Sales, Business Development, and AI/ML Delivery teams to accelerate customer adoption.

   - Act as a technical liaison between customers and our internal service teams, providing valuable customer-driven feedback for product improvement.

4. Community Building:

   - Foster a community of machine learning experts within our organization and help them understand how to integrate Generative AI solutions into customer architectures.

   - Create field enablement materials to educate our broader Solutions Architect population on integrating Generative AI solutions into customer architectures.

Desired Skills and Qualifications:

 Experience:

- 3+ years of experience as a data scientist, specializing in machine learning and AI.

- Proficiency in Python, including experience with machine learning libraries such as scikit-learn, PyTorch, TensorFlow, and NLP frameworks with a minimum of 3 years of practical experience.

- Experience using LangChain, LlamaIndex, RAG, vector databases, and prompt engineering

- 3+ years of Data querying languages such as SQL, GraphQL or similar

- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.

- A minimum of 2 years of hands-on experience in researching and applying large language and generative AI models.

- Strong expertise in Natural Language Processing, including text representation, language modeling, sequence-to-sequence architectures, and semantic understanding.

- 3+ years of practical experience in technical architecture, design, deployment, and operational knowledge in the field of machine learning.

 Preferred Additional Experience:

- Expertise in LangChain, LlamaIndex, RAG, FM tuning, Data Augmentation, and model performance observability.

- Involvement in the open-source LLM community, such as HuggingFace and StableDiffusion

- Experience with large models pretraining/fine-tuning and familiarity with distributed training.

- Exceptional customer-facing skills, capable of engaging with senior level stakeholders across different organizations.

- Proven capacity to approach business, product, and technical complexities strategically within an enterprise setting, backed by a history of pioneering thought leadership and innovation in the field of Machine Learning.

 Technical Skills:

- Programming: Proficiency in Python as the primary programming language.

- Machine Learning Libraries: Expertise in using cutting-edge tools and libraries including scikit-learn, PyTorch, JAX, and TensorFlow to develop and deploy machine learning models.

- Natural Language Processing (NLP): Profound knowledge and practical experience with NLP tools such as spaCy, NLTK, or HuggingFace Transformers for advanced language processing.

- Generative AI: Familiarity with state-of-the-art generative AI models like GPT-3, GPT-4, or OpenAI's DALL-E for image and text generation.

 Plus:

- Cloud experience/DevOps: Proficiency in DevOps tools such as Docker, Kubernetes, and GitHub actions or similar for managing and deploying machine learning models in a containerized environment. Experience in leveraging AWS (Sagemaker), IBM Cloud (WatsonX), Azure (ML/ML Studio) or GCP (Vertex AI) for building out tailored solutions using their cloud services.

- MLOps: Expertise in MLOps tools like MLflow, Kubeflow, or similar to streamline the machine learning lifecycle, including model versioning and automated deployment.