Role- AI Developer
Location: Atlanta GA
Contract-6 Months
AI & GenAI Engineering
Architect develop and deploy AI/ML and Generative AI solutions across personalization forecasting automation and intelligent assistants.
Build and optimize LLM based solutions (prompt engineering RAG orchestration guardrails).
Design scalable end to end ML pipelines: data ingestion feature engineering training evaluation deployment and monitoring.
Lead model performance optimization bias detection drift monitoring and lifecycle management in production.
Enterprise & Cloud Integration
Integrate AI solutions with cloud native platforms APIs data lakes and enterprise systems.
Partner with architecture teams to ensure alignment with enterprise AI security and data standards.
Apply MLOps best practices for CI/CD observability and reliability.
Required Qualifications
8 12 years of professional software engineering experience with 5 years in AI/ML.
Expert level Python skills; strong experience building production AI systems.
Hands on experience with Generative AI / LLMs (RAG prompt design orchestration frameworks).
Strong experience with cloud platforms (GCP / AWS preferred)
Proven experience deploying AI models into enterprise production environments.
Deep understanding of ML algorithms model evaluation and system trade offs.
Experience working in regulated security conscious environments.
Role- AI Developer Location: Atlanta GA Contract-6 Months AI & GenAI Engineering Architect develop and deploy AI/ML and Generative AI solutions across personalization forecasting automation and intelligent assistants. Build and optimize LLM based solutions (prompt engineering RAG orchestrat...
Role- AI Developer
Location: Atlanta GA
Contract-6 Months
AI & GenAI Engineering
Architect develop and deploy AI/ML and Generative AI solutions across personalization forecasting automation and intelligent assistants.
Build and optimize LLM based solutions (prompt engineering RAG orchestration guardrails).
Design scalable end to end ML pipelines: data ingestion feature engineering training evaluation deployment and monitoring.
Lead model performance optimization bias detection drift monitoring and lifecycle management in production.
Enterprise & Cloud Integration
Integrate AI solutions with cloud native platforms APIs data lakes and enterprise systems.
Partner with architecture teams to ensure alignment with enterprise AI security and data standards.
Apply MLOps best practices for CI/CD observability and reliability.
Required Qualifications
8 12 years of professional software engineering experience with 5 years in AI/ML.
Expert level Python skills; strong experience building production AI systems.
Hands on experience with Generative AI / LLMs (RAG prompt design orchestration frameworks).
Strong experience with cloud platforms (GCP / AWS preferred)
Proven experience deploying AI models into enterprise production environments.
Deep understanding of ML algorithms model evaluation and system trade offs.
Experience working in regulated security conscious environments.
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