Senior AI Engineer
New
E
emerchantpayFintech, Payment Services
Fully distributed and remote.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles; 2-3 years in AI development, ML engineering, or data science.
- Required Skills
- AWSPythonFastAPIReactMLOpsLangChain
Requirements
- 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related roles.
- 2-3 years of specific experience in AI development, ML engineering, or data science with production deployments.
- Strong Python development experience including FastAPI, Flask, or Django.
- Hands-on experience with production-grade AI/ML systems and cloud-native architectures.
- Strong knowledge of AWS, including Amazon Bedrock, Bedrock AgentCore, and SageMaker.
- Experience with AI agents, agentic workflows, and LLM-based application orchestration.
- Knowledge of deep learning, generative AI, embeddings, and RAG architectures.
- Experience with advanced LLM frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Proficiency in MLOps, model deployment, pipelines, and evaluation frameworks.
- Experience with vector databases such as Amazon OpenSearch, Pinecone, or pgvector.
- Basic experience with React for internal tools and dashboards.
- Strong communication skills for explaining technical decisions to diverse stakeholders.
Responsibilities
- Design, build, and maintain AI-powered applications, services, and integrations.
- Implement AI agents, agentic workflows, automation, and LLM-based applications.
- Build and integrate AI applications using Python (FastAPI/Flask/Django) and React.
- Implement solutions using AWS AI/ML services like Amazon Bedrock, Bedrock AgentCore, and SageMaker.
- Develop and integrate AI agents that interact with internal APIs and enterprise systems.
- Build and maintain RAG-based solutions including ingestion, retrieval, and grounding.
- Contribute to ML pipelines and MLOps practices including deployment and monitoring.
- Support production rollouts, troubleshooting, and optimization of AI systems.
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