AI Engineer - AWS

We are looking for an AI Engineer with strong hands-on AWS expertise to design, develop, deploy, and scale AI/ML and generative AI solutions in production environments. The ideal candidate will have experience in Python, LLMs, RAG, Agentic AI, AWS AI/ML services, and production-grade deployment, with the ability to take AI solutions from POC to production.

Responsibilities:

• Design, build, and deploy AI/ML and generative AI solutions on AWS.
• Develop LLM-powered applications, AI agents, RAG pipelines, and intelligent automation solutions.
• Build scalable AI/ML pipelines using Python and modern AI frameworks.
• Work with AWS services such as Amazon Bedrock, SageMaker, Lambda, S3, ECS/EKS, EC2, and API Gateway.
• Implement RAG solutions using embedding models and vector databases.
• Develop prompt engineering, model evaluation, fine-tuning, and optimization strategies.
• Build MLOps/LLMOps pipelines for model deployment, monitoring, and continuous improvement.
• Integrate AI models with applications through REST APIs and microservices.
• Optimize AI workloads for performance, scalability, reliability, security, and cost.
• Collaborate with Product, Engineering, Data Science, and DevOps teams to deliver production-ready AI solutions.

Requirements:

• Strong analytical and problem-solving skills.
• Ability to take AI solutions from POC to production.
• Understanding of scalable cloud architecture and production systems.
• Ability to work independently and collaborate across teams.
• Strong communication and documentation skills.
• Key Technology Stack: Python, AWS, Amazon Bedrock, SageMaker, Generative AI, LLMs, RAG, Agentic AI, LangChain, LlamaIndex, Hugging Face, PyTorch, Docker, Kubernetes, REST APIs, Vector Databases, MLOps, and LLMOps.

AI and Generative AI:

• Strong understanding of machine learning, deep learning, NLP, and generative AI.
• Hands-on experience with LLMs, RAG, embeddings, vector databases, and prompt engineering.
• Experience with PyTorch, TensorFlow, Hugging Face, LangChain, or LlamaIndex.
• Understanding of model evaluation, fine-tuning, inference, and optimization. AWS Cloud
• Hands-on experience with Amazon Bedrock and/or SageMaker.
• Experience with AWS Lambda, S3, EC2, ECS/EKS, API Gateway, Step Functions, and CloudWatch.
• Knowledge of IAM, AWS Glue, Athena, and EventBridge is an advantage.

Programming and Engineering:

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or similar frameworks.
• Strong understanding of REST APIs, microservices, Git, Docker, and CI/CD.
• Experience with SQL/NoSQL databases and vector databases such as Pinecone, Weaviate, Milvus, OpenSearch, or pgvector.

Good to Have:

• Experience building GenAI applications using Amazon Bedrock.
• Hands-on experience with Agentic AI, AI agents, and tool/function calling.
• Experience with MLOps/LLMOps, Kubernetes, Terraform, or CloudFormation.
• AWS certifications in machine learning or solutions architecture.
• Knowledge of AI security, data privacy, model governance, and responsible AI.
• Experience in a startup or fast-paced product environment.