Python Developer – AI Engineering

Role Title: Python Developer – AI Engineering / GenAI

100% Remote

Roles & Responsibilities:

1. Design, develop, and maintain Python-based backend services and APIs for AI-enabled applications, with strong emphasis on clean, modular, production-ready code.

2. Build and integrate Generative AI workflows using LLMs, RAG pipelines, prompt orchestration, and structured-output processing.

3. Develop FastAPI-based REST endpoints to support file processing, pipeline execution, asynchronous workflows, and integration with external enterprise applications.

4. Implement retrieval workflows using GCP services such as Vertex AI, Vertex AI Search, Cloud Storage, BigQuery, Pub/Sub, Secret Manager, and Cloud Run.

5. Develop data-processing logic for structured and unstructured inputs, including document parsing, metadata generation, validation, transformation, and downstream persistence.

6. Support integration testing, debugging, deployment, logging, monitoring, and troubleshooting of AI applications across development and QA environments.

7. Collaborate with solution architects, cloud infrastructure teams, product owners, and business stakeholders to translate requirements into working AI features and scalable backend components.

8. Contribute to prompt engineering, output validation, error handling, retry logic, and guardrails to improve reliability of AI pipeline responses.

9. Maintain technical documentation, API contracts, configuration files, deployment artifacts, and reusable engineering components.

Required Experience:

1. 4–7 years of software development experience, with strong hands-on experience in Python backend development.

2. At least 1–2 years of practical experience with Generative AI, LLM-based applications, or RAG solutions.

3. Experience building REST APIs using FastAPI, Flask, or similar Python frameworks, preferably FastAPI.

4. Hands-on experience working with Google Cloud Platform, especially Cloud Run, Cloud Storage, Vertex AI, BigQuery, Pub/Sub, IAM, and Secret Manager.

5. Experience integrating AI/ML services into enterprise applications using APIs, authentication, asynchronous processing, and cloud-native services.

6. Experience processing structured and unstructured data formats such as JSON, JSONL, Excel, CSV, PDF, Word documents, and text.

7. Experience with source control, CI/CD, containerization, and deployment workflows using Git, Docker, GitLab/GitHub, and cloud deployment pipelines.

8. Exposure to debugging distributed or cloud-hosted applications using logs, monitoring, request tracing, and error analysis.

9. Experience working in Agile delivery teams and collaborating with cross-functional stakeholders.

Must-Have Skill Set:

1. Python – strong programming fundamentals, object-oriented design, modular coding, exception handling, testing, and package management.

2. Generative AI / LLMs – prompt engineering, structured outputs, model invocation, response validation, token usage, and basic evaluation concepts.

3. RAG – document ingestion, chunking, embeddings, vector retrieval, metadata filtering, reranking, and grounding.

4. GCP – practical experience with Vertex AI, Vertex AI Search, Cloud Run, GCS, BigQuery, Pub/Sub, Secret Manager, IAM, and Cloud Logging.

5. FastAPI / REST APIs – endpoint development, request/response models, authentication, async processing, status handling, and API integration.

6. Data Processing – pandas, openpyxl, JSON/JSONL handling, document processing, validation, and transformation.

7. Cloud-Native Development – Docker, service accounts, environment variables, secrets, CI/CD, deployment, and runtime troubleshooting.

8. Git – branching, pull requests, merge conflict resolution, version control, and collaborative development.

9. Testing & Debugging – unit testing, API testing, integration testing, Postman/cURL, log analysis, and production issue troubleshooting.

Preferred / Good-to-Have Skills:

1. Experience with Agentic AI frameworks or multi-step AI orchestration.

2. Familiarity with Vertex AI Vector Search, Graph RAG, or knowledge graph concepts.

3. Experience with Pub/Sub-based asynchronous processing and event-driven architectures.

4. Understanding of OAuth, service-to-service authentication, IAM, signed URLs, and secure API integration.

5. Exposure to React or frontend integration is useful but not mandatory.

6. Familiarity with AI evaluation metrics such as groundedness, relevance, completeness, coherence, and confidence scoring.

7. Healthcare-domain exposure is beneficial, especially where the solution involves enterprise workflow, requirements, or document intelligence.