AI and Machine Learning Engineer, Mid

$75,600 - $172,000/Yr

Booz Allen Hamilton - Atlanta, GA

Part-time,Full-time - Mid Level

Atlanta, GA

Professional, Scientific, and Technical Services

About the position

As an experienced engineer, you know that machine learning (ML) is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution. We need your technical knowledge and desire to problem-solve to support solutions to global challenges across a range of private and public sectors, spanning areas like fraud detection, cancer research, and national intelligence. As an ML and AI engineer on our computational life sciences team, you'll train, test, deploy, and maintain models that learn from data, and build purpose-driven generative AI solutions. In this role, you'll own and define the direction of mission-critical solutions by applying best-fit ML algorithms and generative AI technologies. You'll collaborate with a community of research and experimentation leaders, software and IT operations engineers, AI and ML experts, data scientists, solution architects, systems engineers, and product owners to deliver world-class solutions. Your advanced solutioning skills and extensive technical expertise will help drive innovation that is applicable across multiple client domains as they navigate the landscape of AI or ML solutions, tools, and frameworks.

Responsibilities

• Train, test, deploy, and maintain machine learning models that learn from data.
• Build purpose-driven generative AI solutions.
• Define the direction of mission-critical solutions using best-fit ML algorithms.
• Collaborate with a community of research and experimentation leaders, software and IT operations engineers, AI and ML experts, data scientists, solution architects, systems engineers, and product owners.
• Drive innovation applicable across multiple client domains in AI and ML solutions.

Requirements

• 3+ years of experience with AI, ML, or software engineering to build customer-facing solutions and products.
• 2+ years of experience with LLM repos, including HuggingFace and API integration and development with RESTful APIs.
• 2+ years of experience with LLM frameworks and tools, including LangChain, LlamaIndex, FastAPI, Flask, or Streamlit and programming languages, including Python, Scala, or Java.
• 2+ years of experience with integrating LLMs, including Llama, Mixtral, or GPT-4, generative pre-trained transformer (GPT) models, or multi-modal models with applications or user interfaces.
• 2+ years of experience in working with ML libraries such as Scikit-learn, Pandas, TensorFlow, or Keras.
• 1+ years of experience with leveraging vector databases for Retrieval Augmented Generation.
• 1+ years of experience with cloud platforms, including Azure AI Studio or ML, AWS Bedrock or SageMaker, or GCP Vertex AI and MLOps, including CI/CD, model training or testing, deployment, or monitoring.
• 1+ years of experience with HTML, CSS, JavaScript, and common front-end frameworks.
• Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements.
• Bachelor's degree.

Nice-to-haves

• Experience with working in collaborative, interdisciplinary team environments, consisting of data scientists, ML engineers, and domain experts.
• Experience in working with federal clients.
• Experience with container technologies, including Docker or Kubernetes.
• Experience with finetuning LLMs.
• Knowledge of evaluating generative AI models and model output and applying responsible AI principles to LLMs.
• Knowledge of LLMOps.
• Ability to distill complex technical concepts into easily understandable summaries consumable by business leaders with a non-technical background.
• Possession of excellent verbal and written communication skills.

Benefits

• Health insurance coverage.
• Life insurance coverage.
• Disability insurance coverage.
• Financial and retirement benefits.
• Paid leave.
• Professional development opportunities.
• Tuition assistance.
• Work-life programs.
• Dependent care support.