W2 AI/ML Engineer LLM & AWS Integration

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Job Description : AI/ML Engineer LLM & AWS Integration

Description

Hybrid Reston preferred - Plano an option

About The Role

We are seeking a highly skilled and motivated AI/ML Engineer to join our team and drive the development and optimization of AI solutions. This role is ideal for someone who thrives at the intersection of machine learning, large language models (LLMs), and cloud infrastructure. You will collaborate closely with business stakeholders to design, build, and refine intelligent systems that leverage cutting-edge technologies.

Key Responsibilities

Collaborate with business teams to understand requirements and translate them into ML models and prompt-based solutions.

Design, develop, and fine-tune machine learning models, particularly those involving LLMs and generative AI.

Optimize and adapt prompt engineering strategies to improve model performance and relevance.

Integrate and deploy models using AWS services including Bedrock, S3, ECS, EC2, Lambda and other AI/ML related services.

Build and maintain scalable data pipelines and APIs to support ML workflows.

Monitor model performance and iterate based on feedback and metrics.

Stay current with advancements in AI/ML and cloud technologies to ensure our solutions remain cutting-edge.

Required Qualifications

Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field.

3+ years of experience in machine learning, data science, or AI engineering.

Hands-on experience with LLMs (e.g., OpenAI, Anthropic, Cohere) and prompt engineering.

Strong proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).

Deep experience with AWS services, especially Bedrock, S3, EC2, and Lambda

Familiarity with MLOps practices and tools for model deployment and monitoring.

Excellent problem-solving skills and ability to communicate technical concepts to non-technical stakeholders.

Strong programming skills in data analytics related languages and libraries, such as Python, R, Pandas, or JavaScript.

Experience with AWS SageMaker for model development and model deployment.

Understanding of quantitative/statistical/ML/AI modeling methodologies.

Experience in ML engineering, including hands-on experience with Generative AI/LLMs.

Experience with developing and deploying AI Agents for business problems.

Preferred Qualifications

Experience with fine-tuning or customizing foundation models.

Knowledge of data privacy and security best practices in cloud environments.

Familiarity with containerization (Docker) and container orchestration is a plus.