GenAI Python - Senior Engineer

Job Description

Key Responsibilities:

GenAI Application Development
. Build, optimize, and maintain generative AI applications using Python, FastAPI, and modern microservices patterns.
. Integrate AWS Bedrock foundation models and embeddings into enterprise workflows.
. Develop scalable, secure REST APIs to expose GenAI capabilities.
AWS Cloud Engineering
. Design and implement serverless, event-driven AI workflows using AWS Lambda, Step Functions, API Gateway, SQS/SNS, and EventBridge.
. Build and optimize data persistence layers using AWS RDS (PostgreSQL/MySQL).
AI/ML Pipeline Enablement
. Implement prompt engineering, model orchestration, and inference pipelines.
. Fine-tune and evaluate LLMs using Bedrock or other supported frameworks.
. Collaborate with Data Science teams to deploy custom models into AWS environments.
System Architecture & Best Practices
. Contribute to architecture designs for GenAI microservices and data flows.
. Ensure adherence to security, compliance, and cost-optimization best practices in AWS.
. Implement robust monitoring, logging, and observability for AI services.
Collaboration & Stakeholder Engagement
. Work with product managers, data engineers, cloud architects, and UX teams to translate requirements into scalable AI features.
. Document services, workflows, APIs, and operational runbooks.
Innovation & Continuous Improvement
. Evaluate new LLMs, embeddings, vector databases, and prompt orchestration tools.
. Explore advanced Python capabilities and emerging GenAI frameworks.
. Champion experimentation and rapid prototyping of AI-driven features.
Basic Qualifications:
. Bachelor's or Master's in Computer Science, Engineering, AI/ML, or related field.
. 5+ years of hands-on software engineering, with at least 3 years in AI/LLM-focused development.
. Expert-level proficiency in Python (async programming, advanced OOP, design patterns).
. Strong experience with FastAPI and REST API development.
. Hands-on expertise with AWS Lambda, Bedrock, Step Functions, RDS, S3, IAM, and CloudWatch.
. Proven experience building production-grade GenAI applications or LLM-integrated solutions.
. Familiarity with vector databases (FAISS, Pinecone, or AWS OpenSearch vectors).
. Solid understanding of CI/CD, DevOps, and GitOps workflows.
Preferred Qualifications
. Experience with LangChain or Bedrock Agents.
. Knowledge of RAG architecture and embedding pipelines.
. Experience with AWS SageMaker.
. Strong knowledge of containerized deployment (Docker).
. Experience optimizing high-performance Python applications.
. Exposure to MLOps and model evaluation metrics.
. Open-source contributions in AI or Python ecosystems.

Mandatory Competencies