A
Applied AI Engineer
Aktra
6 hours ago
Full-time
On-site
The Applied AI Engineer will design, develop, evaluate, and deploy scalable, enterprise-grade generative AI solutions. The role emphasizes agentic workflows, LLM evaluation, RAG using knowledge and vector bases, and MLOps implementation using AWS Bedrock and SageMaker.
Key Responsibilities
Design and deploy AI agents, agentic workflows, and enterprise generative AI applications.
Develop, fine-tune, and evaluate LLMs, including Claude, Azure OpenAI, and open-weight models.
Build RAG solutions using vector databases, graph databases, and enterprise knowledge bases.
Implement MCP and Agent-to-Agent (A2A) communication.
Develop AI and ML workflows using AWS SageMaker and Bedrock, along with MLflow and Kubeflow on Kubernetes and EKS.
Create data ingestion, embedding, and multimodal processing pipelines using Python, Spark, and PySpark.
Integrate AI solutions with enterprise platforms through APIs and standardized architectural patterns.
Implement LLM evaluation, trace evaluation, safety guardrails, and validation frameworks such as DeepEval.
Apply CI/CD, Git, Spec-Based Development, and cloud-native engineering best practices.
Required Qualifications
Bachelor's degree in Computer Science, Data Science, AI/ML, or related field;
Master's preferred .
Strong proficiency in
Python , Pandas, NumPy, and machine learning frameworks such as Transformers and LangChain.
Hands-on experience with generative AI, LLMs, prompt engineering, fine-tuning, and AI agent development.
Experience with AWS, Azure OpenAI, AWS Bedrock, and SageMaker.
Expertise in RAG architectures, vector databases, knowledge bases, and multimodal AI.
Experience with LLM evaluation frameworks such as DeepEval and AI safety or guardrails.
Knowledge of Git, CI/CD, Spec-Based Development, and cloud-native architectures.
Familiarity with big data technologies such as Spark and Hadoop.
Strong collaboration skills and experience working in Agile environments.
GitHub code repository link required
for all candidates; thorough technical vetting is mandatory.
Technology Stack
Generative AI, LLMs, Claude, Azure OpenAI, open-weight models
MCP, Agent-to-Agent (A2A)
AWS SageMaker, AWS Bedrock, MLflow, Kubeflow, Kubernetes, EKS
Python, Spark, PySpark
Vector databases, graph databases
DeepEval, Transformers, LangChain
Git, CI/CD, Spec-Based Development
AWS, Azure
Preferred Qualifications
Experience in financial services, regulatory technology, or enterprise automation.
Familiarity with MLOps tools such as MLflow, Kubeflow, and Weights & Biases.
Experience with Docker, Kubernetes, and Apache Spark.
Contributions to open-source AI projects or research publications.
Experience with graph databases and production-grade AI deployments.
Work Location and Environment On-site role in
McLean, VA , working in a collaborative, cross-functional Agile environment.
#J-18808-Ljbffr
Key Responsibilities
Design and deploy AI agents, agentic workflows, and enterprise generative AI applications.
Develop, fine-tune, and evaluate LLMs, including Claude, Azure OpenAI, and open-weight models.
Build RAG solutions using vector databases, graph databases, and enterprise knowledge bases.
Implement MCP and Agent-to-Agent (A2A) communication.
Develop AI and ML workflows using AWS SageMaker and Bedrock, along with MLflow and Kubeflow on Kubernetes and EKS.
Create data ingestion, embedding, and multimodal processing pipelines using Python, Spark, and PySpark.
Integrate AI solutions with enterprise platforms through APIs and standardized architectural patterns.
Implement LLM evaluation, trace evaluation, safety guardrails, and validation frameworks such as DeepEval.
Apply CI/CD, Git, Spec-Based Development, and cloud-native engineering best practices.
Required Qualifications
Bachelor's degree in Computer Science, Data Science, AI/ML, or related field;
Master's preferred .
Strong proficiency in
Python , Pandas, NumPy, and machine learning frameworks such as Transformers and LangChain.
Hands-on experience with generative AI, LLMs, prompt engineering, fine-tuning, and AI agent development.
Experience with AWS, Azure OpenAI, AWS Bedrock, and SageMaker.
Expertise in RAG architectures, vector databases, knowledge bases, and multimodal AI.
Experience with LLM evaluation frameworks such as DeepEval and AI safety or guardrails.
Knowledge of Git, CI/CD, Spec-Based Development, and cloud-native architectures.
Familiarity with big data technologies such as Spark and Hadoop.
Strong collaboration skills and experience working in Agile environments.
GitHub code repository link required
for all candidates; thorough technical vetting is mandatory.
Technology Stack
Generative AI, LLMs, Claude, Azure OpenAI, open-weight models
MCP, Agent-to-Agent (A2A)
AWS SageMaker, AWS Bedrock, MLflow, Kubeflow, Kubernetes, EKS
Python, Spark, PySpark
Vector databases, graph databases
DeepEval, Transformers, LangChain
Git, CI/CD, Spec-Based Development
AWS, Azure
Preferred Qualifications
Experience in financial services, regulatory technology, or enterprise automation.
Familiarity with MLOps tools such as MLflow, Kubeflow, and Weights & Biases.
Experience with Docker, Kubernetes, and Apache Spark.
Contributions to open-source AI projects or research publications.
Experience with graph databases and production-grade AI deployments.
Work Location and Environment On-site role in
McLean, VA , working in a collaborative, cross-functional Agile environment.
#J-18808-Ljbffr