Generative AI Engineer

Role: Senior Machine Learning, MLOps, and Generative AI Engineer

Location: Atlanta, GA 30342 (100% Onsite)

Duration: Long Term

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.

Technical Skills:
• AWS: SageMaker, ECR, Lambda, CloudFormation, IAM, S3, and monitoring tools (CloudWatch, SageMaker Model Monitor).
• MLOps: Model versioning (MLflow), orchestration (TFX, Kubeflow), and drift detection (Evidently, AWS SageMaker Model Monitor).
• Generative AI: Hands-on experience with LangChain, LLMs (GPT, Claude, Llama), and vector databases (Pinecone, FAISS).
• APIs: FastAPI/Flask, API Gateway, and optimization techniques (caching, async processing, load balancing).
• Programming: Python (PyTorch, TensorFlow), infrastructure-as-code (Terraform, CDK), and containerization (Docker, Kubernetes)
• Soft Skills: Strong problem-solving, communication, and leadership abilities

Thanks

sagar