Remote Python Developer – AI Engineering / GenAI

Sigma, Inc. is recruiting for an unnamed client seeking a Python Developer to build and maintain AI-powered backend services and Generative AI workflows. The role focuses on Python APIs, LLM and RAG development, GCP integration, document processing, deployment, troubleshooting, and collaboration with technical and business stakeholders.

Responsibilities:

• Design, develop, and maintain Python-based backend services and APIs for AI-enabled applications
• Write clean, modular, reusable, and production-ready code using sound software engineering practices
• Develop REST APIs using FastAPI or similar Python frameworks to support file processing, AI pipeline execution, and integration with enterprise applications
• Implement asynchronous processing, request validation, error handling, and exception management
• Build and integrate Generative AI workflows using LLMs, prompt engineering, and structured-output processing
• Develop Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, text chunking, embeddings, vector retrieval, metadata filtering, and response grounding
• Implement prompt orchestration, response validation, retry mechanisms, and guardrails to improve AI application reliability
• Support AI output evaluation and troubleshooting to improve response relevance, accuracy, and completeness
• Develop and deploy AI-enabled applications using GCP services, including Vertex AI, Vertex AI Search, Cloud Run, Cloud Storage, BigQuery, Pub/Sub, Secret Manager, and IAM
• Integrate AI and machine learning services into enterprise applications using secure APIs and cloud-native components
• Support cloud deployments, configuration management, service accounts, environment variables, and secrets management
• Work with cloud infrastructure teams to troubleshoot application issues and support reliable deployments
• Develop data-processing workflows for structured and unstructured data, including JSON, JSONL, Excel, CSV, PDF, Word documents, and text files
• Implement document parsing, metadata generation, data validation, transformation, and downstream data persistence
• Use Python libraries such as pandas and openpyxl to process and validate data
• Support document-based AI workflows and ensure data is prepared appropriately for retrieval and AI processing
• Perform unit testing, API testing, and integration testing across development and QA environments
• Debug and resolve issues related to APIs, AI workflows, cloud services, and data-processing pipelines
• Use application logs, monitoring tools, request tracing, Postman, and cURL to identify and resolve technical issues
• Support CI/CD pipelines, containerization, and application deployment using Git, Docker, GitHub/GitLab, and cloud deployment tools
• Collaborate with solution architects, cloud infrastructure teams, product owners, and business stakeholders to translate requirements into working AI solutions
• Maintain technical documentation, API contracts, configuration files, deployment artifacts, and reusable software components
• Participate in Agile development activities, code reviews, and collaborative engineering practices
• Contribute to continuous improvement of AI workflows, application performance, code quality, and engineering standards

Requirements:

• 4–7 years of software development experience, with strong hands-on experience in Python backend development
• At least 1–2 years of practical experience developing Generative AI applications, LLM-based solutions, or RAG pipelines
• Experience developing REST APIs using FastAPI, Flask, or similar Python frameworks
• Hands-on experience with Google Cloud Platform, particularly Vertex AI, Vertex AI Search, Cloud Run, Cloud Storage, BigQuery, Pub/Sub, IAM, and Secret Manager
• Experience integrating AI/ML services with enterprise applications through APIs, authentication, and asynchronous processing
• Experience working with structured and unstructured data formats and document-processing workflows
• Familiarity with Git, Docker, CI/CD pipelines, and cloud application deployment
• Experience troubleshooting cloud-hosted or distributed applications using logs, monitoring, and error analysis
• Experience working in Agile teams and collaborating with cross-functional stakeholders
• Python Development: Strong programming fundamentals, object-oriented design, modular coding, exception handling, testing, and package management
• Generative AI / LLMs: Prompt engineering, model invocation, structured outputs, response validation, token usage, and basic AI evaluation concepts
• RAG Pipelines: Document ingestion, chunking, embeddings, vector retrieval, metadata filtering, reranking, and grounding
• Google Cloud Platform: Practical experience with Vertex AI, Vertex AI Search, Cloud Run, Google Cloud Storage (GCS), BigQuery, Pub/Sub, Secret Manager, IAM, and Cloud Logging
• FastAPI / REST APIs: API endpoint development, request and response models, authentication, asynchronous processing, status handling, and API integration
• Data Processing: pandas, openpyxl, JSON/JSONL handling, document processing, data validation, and transformation
• Cloud-Native Development: Docker, service accounts, environment variables, secrets management, CI/CD, deployment, and runtime troubleshooting
• Git and Version Control: Branching, pull requests, merge conflict resolution, and collaborative development
• Testing and Debugging: Unit testing, API testing, integration testing, Postman/cURL, log analysis, and application issue resolution
• A degree in Computer Science, Information Technology, Software Engineering, or a related technical field is beneficial
• Experience with Agentic AI frameworks or multi-step AI orchestration
• Familiarity with Vertex AI Vector Search, Graph RAG, or knowledge graph concepts
• Experience with Pub/Sub-based asynchronous processing and event-driven architectures
• Understanding of OAuth, service-to-service authentication, IAM, signed URLs, and secure API integration
• Exposure to React or frontend integration
• Familiarity with AI evaluation metrics such as groundedness, relevance, completeness, coherence, and confidence scoring
• Healthcare-domain experience, particularly with enterprise workflows, requirements, or document intelligence solutions
• FastAPI experience