Python Developer – AI Engineering, GenAI

Sigma Systems

. Design, develop, and maintain Python-based backend services and APIs for AI-enabled applications .

Posted 10/15/2026contractRemote • Pennsylvania • United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

BigQueryCloudDockerFlaskGoogle Cloud PlatformPandasPython

About the role

Key responsibilities & impact

Design, develop, and maintain Python-based backend services and APIs for AI-enabled applications
Build and integrate Generative AI workflows using LLMs, RAG pipelines, prompt orchestration, and structured-output processing
Develop FastAPI-based REST endpoints for file processing, pipeline execution, asynchronous workflows, and enterprise application integration
Implement retrieval workflows using GCP services including Vertex AI, Vertex AI Search, Cloud Storage, BigQuery, Pub/Sub, Secret Manager, and Cloud Run
Develop processing logic for structured and unstructured inputs, including document parsing, metadata generation, validation, transformation, and downstream persistence
Support integration testing, debugging, deployment, logging, monitoring, and troubleshooting of AI applications across development and QA environments
Collaborate with solution architects, cloud infrastructure teams, product owners, and business stakeholders to translate requirements into AI features and scalable backend components
Contribute to prompt engineering, output validation, error handling, retry logic, and guardrails to improve AI pipeline reliability
Maintain technical documentation, API contracts, configuration files, deployment artifacts, and reusable engineering components

Requirements

What you’ll need

4–7 years of software development experience, with strong hands-on experience in Python backend development
At least 1–2 years of practical experience with Generative AI, LLM-based applications, or RAG solutions
Experience building REST APIs using FastAPI, Flask, or similar Python frameworks, preferably FastAPI
Hands-on experience with Google Cloud Platform, especially Cloud Run, Cloud Storage, Vertex AI, BigQuery, Pub/Sub, IAM, and Secret Manager
Experience integrating AI/ML services into enterprise applications using APIs, authentication, asynchronous processing, and cloud-native services
Experience processing structured and unstructured data formats such as JSON, JSONL, Excel, CSV, PDF, Word documents, and text
Experience with source control, CI/CD, containerization, and deployment workflows using Git, Docker, GitLab/GitHub, and cloud deployment pipelines
Exposure to debugging distributed or cloud-hosted applications using logs, monitoring, request tracing, and error analysis
Experience working in Agile delivery teams and collaborating with cross-functional stakeholders
Strong Python programming fundamentals, object-oriented design, modular coding, exception handling, testing, and package management
Generative AI/LLM knowledge including prompt engineering, structured outputs, model invocation, response validation, token usage, and basic evaluation concepts
RAG knowledge including document ingestion, chunking, embeddings, vector retrieval, metadata filtering, reranking, and grounding
Practical GCP experience with Vertex AI, Vertex AI Search, Cloud Run, GCS, BigQuery, Pub/Sub, Secret Manager, IAM, and Cloud Logging
FastAPI/REST API skills including endpoint development, request/response models, authentication, asynchronous processing, status handling, and API integration
Data-processing skills with pandas, openpyxl, JSON/JSONL handling, document processing, validation, and transformation
Cloud-native development skills including Docker, service accounts, environment variables, secrets, CI/CD, deployment, and runtime troubleshooting
Git skills including branching, pull requests, merge conflict resolution, version control, and collaborative development
Testing and debugging skills including unit testing, API testing, integration testing, Postman/cURL, log analysis, and production issue troubleshooting

Benefits

Comp & perks

4-month contract assignment