Software Engineer III - Python Backend, AI​/ML Platforms AWS

Job responsibilities

• Work with AWS services including Lambda, API Gateway, ECS, RDS/Aurora, Dynamo
  
  DB, S3, and Cloud Watch.
• Develop backend services using Python (FastAPI, Flask) for model APIs, agent integrations, and automation workflows.
• Contribute to building AI agent features and intelligent automation components.
• Integrate LLM APIs (Amazon Bedrock, OpenAI) into backend services and workflows.
• Implement message queuing and event handling using Amazon SQS, SNS, Kinesis, or Kafka (MSK).
• Assist in building model deployment pipelines and infrastructure automation using AWS services.
• Write infrastructure‑as‑code using Terraform or AWS Cloud Formation under guidance from senior engineers.
• Implement logging and monitoring using Cloud Watch, X‑Ray, and other observability tools.
• Write unit tests, integration tests, and participate in code reviews.
• Collaborate with ML engineers and senior developers to understand requirements and implement solutions.
• Learn and apply AWS best practices for security (IAM, VPC, Secrets Manager) and cost optimization.

• Formal training or certification on software engineering concepts and 3+ years applied experience, including backend software development using Python.
• Working knowledge of AWS Cloud services with hands‑on experience in at least 3‑4 core services (Lambda, API Gateway, RDS, Dynamo
  
  DB, S3, SQS/SNS, or Cloud Watch).
• Basic understanding of SQL databases (Postgre
  
  SQL, MySQL, or Aurora).
• Experience building RESTful APIs and understanding of microservices principles.
• Familiarity with Docker and containerization concepts.
• Understanding of version control (Git) and basic CI/CD concepts.
• Eagerness to learn about AI/ML systems, agent‑based architectures, and MLOps.
• Strong problem‑solving mindset and attention to detail.
• Good communication and teamwork skills.

• AWS certification (Cloud Practitioner, Solutions Architect Associate, or Developer Associate).
• Exposure to Kafka, Amazon Kinesis, MSK, or other messaging/streaming systems.
• Familiarity with LLM APIs (Amazon Bedrock, OpenAI, Anthropic) or AI frameworks (Lang Chain).
• Basic understanding of ML model deployment, Sage Maker, or MLOps concepts.
• Experience with Terraform or AWS Cloud Formation.
• Knowledge of microservices architecture patterns and distributed systems.
• Exposure to Kubernetes (EKS) or container orchestration.
• Understanding of AWS security best practices (IAM roles, VPC, security groups).