Cordia Resources by Cherry Bekaert
Senior Machine Learning Engineer
Cordia Resources by Cherry Bekaert - Alexandria, Virginia, us, 22350
Work at Cordia Resources by Cherry Bekaert
Overview
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Overview
Job Title:
Senior Machine Learning Engineer - LLMs, AWS, OpenAI & LangChain Clearance Requirement:
Eligible for Public Trust Clearance Location:
Remote (occasional on-site support as needed) Position Type:
Full-Time
About the Role We are seeking a
Senior Machine Learning Engineer
with a passion for building cutting-edge solutions using
Large Language Models (LLMs), LangChain, and OpenAI technologies . This role is ideal for an AI expert with at least 8 years of experience, ready to take ownership of advanced AI workflows in a secure, compliant, and cloud-native environment supporting public sector missions.
You will lead the development of intelligent, scalable, and ethically responsible machine learning systems deployed across
AWS cloud infrastructure
to support mission-critical federal use cases.
Key Responsibilities Design, fine-tune, and deploy LLMs (e.g., GPT-4, LLaMA, T5) for generative AI, summarization, classification, and conversational applications. Develop intelligent agent-based applications using LangChain, including prompt templates, tools, chains, memory modules, and RAG pipelines. Architect, develop, and deploy AI/ML systems within AWS, including SageMaker, Lambda, Bedrock, Redshift, S3, and ECS/EKS. Integrate and optimize OpenAI APIs (GPT-4, embeddings, function calling) for public sector use cases. Build robust MLOps pipelines for training, evaluation, deployment, and monitoring using MLflow, SageMaker Pipelines, and Step Functions. Implement and manage vector databases (e.g., FAISS, Pinecone, OpenSearch) for embedding search and retrieval. Ensure all AI/ML implementations meet federal security, privacy, and compliance standards. Conduct experimentation with prompt engineering, hyperparameter tuning, and inference optimization. Collaborate with engineering, DevOps, and compliance teams to deploy and maintain production-ready AI services. Document architecture, workflows, and decisions to support transparency and auditability. Stay current with emerging trends in LLMs, LangChain, OpenAI, and AWS AI/ML services. Required Skills and Experience
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field. Minimum of 8 years of professional experience in machine learning and AI system development. Strong background in NLP and transformer-based architectures, including GPT-style LLMs. Hands-on experience with LangChain for building AI agents and prompt orchestration. Proficiency in OpenAI APIs (ChatGPT, GPT-4, fine-tuning, embeddings, function calling). Demonstrated experience with AWS services including SageMaker, Bedrock, Lambda, S3, Redshift, ECS, and EKS. Proficient in Python and libraries/frameworks such as Hugging Face Transformers, PyTorch, TensorFlow, FastAPI, and scikit-learn. Strong understanding of MLOps principles, including version control, CI/CD, containerization, and pipeline automation. Must be eligible to obtain and retain a Public Trust clearance. Preferred Qualifications
Experience supporting federal agencies or public sector AI initiatives. Expertise in retrieval-augmented generation (RAG), prompt engineering, and embedding-based search. Familiarity with LangSmith, OpenAI fine-tuning, and multi-agent systems. Experience deploying scalable containerized services using Docker and Kubernetes. Knowledge of ethical AI, bias mitigation, and AI governance best practices.
Senior Machine Learning Engineer - LLMs, AWS, OpenAI & LangChain Clearance Requirement:
Eligible for Public Trust Clearance Location:
Remote (occasional on-site support as needed) Position Type:
Full-Time
About the Role We are seeking a
Senior Machine Learning Engineer
with a passion for building cutting-edge solutions using
Large Language Models (LLMs), LangChain, and OpenAI technologies . This role is ideal for an AI expert with at least 8 years of experience, ready to take ownership of advanced AI workflows in a secure, compliant, and cloud-native environment supporting public sector missions.
You will lead the development of intelligent, scalable, and ethically responsible machine learning systems deployed across
AWS cloud infrastructure
to support mission-critical federal use cases.
Key Responsibilities Design, fine-tune, and deploy LLMs (e.g., GPT-4, LLaMA, T5) for generative AI, summarization, classification, and conversational applications. Develop intelligent agent-based applications using LangChain, including prompt templates, tools, chains, memory modules, and RAG pipelines. Architect, develop, and deploy AI/ML systems within AWS, including SageMaker, Lambda, Bedrock, Redshift, S3, and ECS/EKS. Integrate and optimize OpenAI APIs (GPT-4, embeddings, function calling) for public sector use cases. Build robust MLOps pipelines for training, evaluation, deployment, and monitoring using MLflow, SageMaker Pipelines, and Step Functions. Implement and manage vector databases (e.g., FAISS, Pinecone, OpenSearch) for embedding search and retrieval. Ensure all AI/ML implementations meet federal security, privacy, and compliance standards. Conduct experimentation with prompt engineering, hyperparameter tuning, and inference optimization. Collaborate with engineering, DevOps, and compliance teams to deploy and maintain production-ready AI services. Document architecture, workflows, and decisions to support transparency and auditability. Stay current with emerging trends in LLMs, LangChain, OpenAI, and AWS AI/ML services. Required Skills and Experience
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field. Minimum of 8 years of professional experience in machine learning and AI system development. Strong background in NLP and transformer-based architectures, including GPT-style LLMs. Hands-on experience with LangChain for building AI agents and prompt orchestration. Proficiency in OpenAI APIs (ChatGPT, GPT-4, fine-tuning, embeddings, function calling). Demonstrated experience with AWS services including SageMaker, Bedrock, Lambda, S3, Redshift, ECS, and EKS. Proficient in Python and libraries/frameworks such as Hugging Face Transformers, PyTorch, TensorFlow, FastAPI, and scikit-learn. Strong understanding of MLOps principles, including version control, CI/CD, containerization, and pipeline automation. Must be eligible to obtain and retain a Public Trust clearance. Preferred Qualifications
Experience supporting federal agencies or public sector AI initiatives. Expertise in retrieval-augmented generation (RAG), prompt engineering, and embedding-based search. Familiarity with LangSmith, OpenAI fine-tuning, and multi-agent systems. Experience deploying scalable containerized services using Docker and Kubernetes. Knowledge of ethical AI, bias mitigation, and AI governance best practices.