Data Science Solution Specialist – Generative AI

Data Scientist (Generative AI) -Solution Specialist – USDC

Are you an experienced, passionate pioneer in technology – a solutions builder, a roll-up-your-sleeves technologist who wants a daily collaborative environment, think-tank feel and share new ideas with your colleagues – without the extensive demands of travel? If so, consider an opportunity with our US Delivery Center – we are breaking the mold of a typical Delivery Center.

Our US Delivery Centers have been growing since 2014 with significant, continued growth on the horizon. Interested? Read more about our opportunity below …

Work youll do

The Generative AI Engineer will, as part of several client delivery teams, be responsible for developing, designing, and maintaining cutting-edge AI-based systems, ensuring smooth and engaging user experiences. Additionally, the Generative AI Engineer will participate in a wide variety of Natural Language Processing activities, including refining and optimizing prompts to improve the outcome of Large Language Models (LLMs), and code and design review. The kinds of activities performed by the Prompt Engineer will also include, but not be limited to:

Working across client teams to develop and architect Generative AI solutions using ML and GenAI

Developing and promoting standards across the community

Evaluating and selecting appropriate AI tools and machine learning models for tasks, as well as building and training working versions of those models using Python and other open-source technologies

Working with leadership and stakeholders to identify AI opportunities and promote strategy.

Developing and conducting trainings for users across the Government & Public Services landscape on principles used to develop models and how to interact with models to facilitate their business processes.

Building and prioritizing backlog for future machine-learning enabled features to support client business processes.

Youll design and build generative models, selecting the most suitable architecture (e.g., GANs, VAEs) based on the desired output (text, images, code). This involves writing code using Python libraries like TensorFlow or PyTorch.

Once your model is built, youll train it on the prepared data, fine-tuning hyperparameters to achieve optimal performance. Youll then evaluate the models outputs to assess its effectiveness and identify areas for improvement.

Youll collaborate with other engineers to integrate your generative AI solution into existing systems or develop new applications. This might involve deploying the model on cloud platforms for scalability.

The field of generative AI is rapidly evolving. Staying abreast of the latest research, advancements, and ethical considerations in AI development is an ongoing process.

The TeamArtificial Intelligence & Data Engineering

In this age of disruption, organizations need to navigate the future with confidence, embracing decision making with clear, data-driven choices that deliver enterprise value in a dynamic business environment.

The Artificial Intelligence & Data Engineering team leverages the power of data, analytics, robotics, science and cognitive technologies to uncover hidden relationships from vast troves of data, generate insights, and inform decision-making. Together with the Strategy practice, our Strategy & Analytics portfolio helps clients transform their business by architecting organizational intelligence programs and differentiated strategies to win in their chosen markets.

Artificial Intelligence & Data Engineering will work with our clients to:

Implement large-scale data ecosystems including data management, governance and the integration of structured and unstructured data to generate insights leveraging cloud-based platforms

Leverage automation, cognitive and science-based techniques to manage data, predict scenarios and prescribe actions

Drive operational efficiency by maintaining their data ecosystems, sourcing analytics expertise and providing As-a-Service offerings for continuous insights and improvements

Qualifications

Required:

3 years of experience programming in Python or R.

Knowledge of Python libraries like Pandas, Scikit-Learn, Numpy, NLTK is required

3 years of experience with Natural Language Processing (NLP) and Large Language Models (LLM) 3 years of experience building and maintaining scalable API solutions

Experience working with RAG technologies and LLM frameworks (Langchain, Claude and LLamaIndex), LLM model registries (Hugging Face), LLM APIs, embedding models, and vector databases (FAISS , Milvus , OpenSearch, Pinecone etc.)

Experience working with Retrieval Augmented Thoughts (RAT) and chain of thoughts.

Experience building scalable data models and performing complex relational databases queries using SQL (Oracle, MySQL, PostGres), etc.

Experience working with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes).

Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines

Experience driving DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI

Experience with machine learning libraries and services like TensorFlow, PyTorch, or Amazon SageMaker.

Experience integrating GenAI solution on cloud platform (e.g., AWS, Azure, Google Cloud)

3 years of experience designing solutions to address client requirements

1 years of experience with the design and implementation (building, containerizing, and deploying end to end automated data and ML pipelines) of automated cloud solutions

3 years of experience in developing algorithms using data science technologies to build analytical models

3 years of data extraction/manipulation experience using scripts specific to AI/ML

3 years of modeling experience using a variety of regression and supervised and unsupervised learning techniques.

3 years of experience in data wrangling/cleansing, statistical modeling, and programming

3 years of extensive experience working in an Agile development environment

3 years of experience for fluency in both structured and unstructured data (SQL, NOSQL)

3 years of production experience with Apache Spark

3 years of hands-on experience with web APIs, CI/CD for ML, and Serverless Deployment

3 years of experience with presentation and data analysis software such as: SAS, R, SPSS, MATLAB, QlikView, Excel and Access

1 years of experience/familiarity with Linux OS and Windows servers

1 years of experience with/knowledge of Docker, Jenkins, Kubernetes, and other DevOps tools

Must live in a commutable distance (approximately 100-mile radius) to one of the following Delivery locations: Atlanta, GA; Charlotte, NC; Dallas, TX; Gilbert, AZ; Houston, TX; Lake Mary, FL; Mechanicsburg, PA; Philadelphia, PA; with the ability to commute to assigned location for the day, without the need for overnight accommodations

Expectation to co-locate in your designated Delivery location up to 30% of the time based on business needs. This may include a maximum of 10% overnight client/project travel

Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve. This may include overnight travel

Bachelors degree, preferably in Computer Sciences, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience

Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future

Preferred:

Previous Government Consulting and/or professional services experience

In depth understanding of AI protocols and standards

Understanding of technology risks and the ability to assess and mitigate them

Deep knowledge of a specific domain or industry, with a focus on applying NLP/LLM solutions in that context

Experience with debugging and troubleshooting software or solutions design issues

Proven ability to stay current with best practices and new technology solutions in the field

Ability to display both breadth and depth of knowledge regarding functional and technical issues

Experience presenting to clients or other decision makers to present and sell ideas to various audiences (technical and non-technical)

Certification from any of the three major cloud platforms (AWS / Azure / GCP) in Cloud Architecture / Engineering / DevOps / ML.

Familiarity with Kubeflow or MLflow

Experience with machine learning pipelines (Azure ML)

Familiarity with the latest Natural Language Processing or Computer Vision related algorithms

Information for applicants with a need for accommodation: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.