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From:
Nitya,
Nitya software solution
ryan@nityainc.com
Reply to: ryan@nityainc.com
Role: Senior Machine Learning, MLOps, and Generative AI Engineer
Location: Atlanta, GA 30342 (100% Onsite)
C2C
Job Description:
- Senior Machine Learning, MLOps, and Generative AI Engineer with hands-on experience in end-to-end development, deployment, and optimization of ML models in AWS environments.
- Deep understanding of AWS machine learning services, MLOps best practices, and generative AI frameworks (e.g., LangChain, LLMs), along with expertise in API design and optimization.
- Lead architecture, deployment, and monitoring of scalable ML systems while driving innovation in AI-driven solutions
Key Responsibilities
End-to-End ML Development & Deployment:
- Design, build, and deploy machine learning models on AWS (SageMaker, ECR, Lambda, etc.) for production-grade systems.
- Implement CI/CD pipelines for ML workflows using tools like AWS Code Pipeline, GitHub Actions, or Jenkins.
MLOps & Model Lifecycle Management:
- Monitor and mitigate model drift using techniques like A/B testing, retraining pipelines, and performance metrics.
- Optimize model inference latency and scalability using AWS services (e.g., SageMaker Endpoints, ECS/Fargate).
Generative AI & LLM Solutions:
- Develop and fine-tune Large Language Models (LLMs) for chatbots, content generation, and other NLP tasks.
- Leverage frameworks like LangChain to build context-aware, chain-based AI applications.
- Architect retrieval-augmented generation (RAG) pipelines for enterprise use cases.
API Development & Optimization:
- Design and deploy REST/GraphQL APIs for ML models using FastAPI, Flask, or AWS API Gateway.
- Optimize API performance (latency, throughput) and ensure seamless integration with downstream systems.
Cross-Functional Collaboration:
- Partner with data engineers, DevOps, and product teams to ensure alignment on architecture, security, and scalability.
- Mentor junior engineers and lead technical discussions on ML/AI best practices.
Documentation & Innovation:
- Document architectures, deployment processes, and model governance policies.
- Stay ahead of industry trends (e.g., vector databases, RLHF, multi-modal AI) and prototype innovative solutions.