Senior AI Engineer
New
E
emerchantpayFintech
Fully distributed and remote.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- Minimum 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles; 2-3 years of experience in AI development, ML engineering, or data science.
- Required Skills
- AWSPythonReactMLOpsGenerative AILangChain
Requirements
- Minimum 7-8 years of professional experience in software engineering, AI engineering, ML engineering, or data science.
- At least 2-3 years of experience in AI development or ML engineering with a track record of production deployments.
- Strong hands-on experience building production-grade AI, ML, and data-driven systems.
- Practical experience with AI agents, agentic workflows, LLM-based applications, and orchestration patterns.
- Strong Python development experience with frameworks like FastAPI, Flask, or Django.
- Experience with React for building user-facing AI tools and dashboards.
- In-depth knowledge of AWS AI/ML services, specifically Amazon Bedrock, Bedrock AgentCore, and SageMaker.
- Proficiency with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen.
- Experience with ML pipelines, MLOps, experiment tracking, and model lifecycle management.
- Experience building RAG systems including vector databases (e.g., Pinecone, pgvector, Amazon OpenSearch).
- Understanding of security and governance for AI, including data privacy and access control.
Responsibilities
- Design, build, and maintain AI-powered applications, services, and integrations.
- Implement solutions focused on AI agents, agentic workflows, automation, and AI-assisted business processes.
- Build and integrate AI applications using Python frameworks, React, and relevant AI/ML libraries.
- Implement AI solutions using AWS services like Amazon Bedrock and Amazon SageMaker for model hosting, inference, and orchestration.
- Develop and integrate AI agents that interact with internal APIs, knowledge bases, and external tools.
- Build and maintain RAG-based solutions, including ingestion, vector search, retrieval logic, and reranking.
- Contribute to ML pipelines and MLOps practices such as experiment tracking, model deployment, and monitoring.
- Implement evaluation approaches for LLM outputs, agent behavior, and model performance to ensure safety and reliability.
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