AI Developer with GEN AI , Data AI - RAG , LLMs , AI/ML
Location KC, USA
Onsite work
Rate: 60 $ phr on Corp- Corp
Role Overview
We are seeking a highly skilled AI Developer with strong expertise in Generative AI and Data AI to design, develop, and deploy intelligent solutions that leverage cutting-edge machine learning models. The ideal candidate will have hands-on experience with LLMs, RAG pipelines, Agentic AI systems, and data-driven AI applications, and will play a key role in building scalable, secure, and high-performing AI platforms.
Key Responsibilities
Generative AI Solutions: Design and implement applications using LLMs (e.g., GPT, Claude, Llama), including fine-tuning, prompt engineering, and multi-agent orchestration.
Data AI Development: Build and optimize data-driven AI models for analytics, forecasting, anomaly detection, and decision support.
RAG Pipelines: Develop retrieval-augmented generation workflows using vector databases (FAISS, Pinecone, Weaviate) to improve contextual accuracy.
AI/ML Infrastructure: Deploy and manage AI workloads on cloud platforms (AWS, Azure, Google Cloud Platform) with MLOps practices (CI/CD, monitoring, drift detection).
Integration & APIs: Build APIs and microservices to integrate AI models into enterprise applications.
Security & Compliance: Implement responsible AI practices including guardrails, bias detection, PII handling, and auditability.
Collaboration: Work closely with data engineers, product managers, and business stakeholders to align AI solutions with organizational goals.
Required Skills
Strong programming skills in Python.
Hands-on with LLMs, Transformers, LangChain, Hugging Face, OpenAI/Azure OpenAI.
Experience with data pipelines, ETL, and analytics tools (Databricks, Spark, SQL).
Proficiency in vector databases (FAISS, Pinecone, Weaviate).
Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) and containerization (Docker, Kubernetes).
Familiarity with MLOps tools (MLflow, Kubeflow, Jenkins, GitHub Actions).
Strong understanding of DevSecOps practices and compliance frameworks.
Excellent problem-solving, communication, and stakeholder management skills.
Preferred Qualifications
Experience in multi-agent AI orchestration and agentic workflows.
Exposure to AI safety testing (hallucination detection, toxicity validation, bias mitigation).
Prior work in enterprise-scale AI deployments with measurable business impact.
Master s degree in Computer Science, Data Science, or Artificial Intelligence.
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