Job Summary (List Format):

Job Requirements:

- Hands-on Python experience, including APIs, async programming, and robust engineering fundamentals.
- 1–3 years of experience with GenAI or LLM applications.
- Practical experience with LangChain or LangGraph for building chains, agents, and stateful LLM workflows.
- Experience with cloud AI platforms, specifically Google Vertex AI (Models, Vector Search, Embeddings, Pipelines) and Azure OpenAI (GPT-4, GPT-4 Turbo, Assistants API, embeddings).
- Strong knowledge of RAG pipelines, multi-step reasoning agents, tools/function calling, and prompt engineering.
- Experience with vector databases such as PGVector or MongoDB.
- Understanding of end-to-end GenAI workflows: ingestion, chunking, embedding, retrieval, and inference.
- Proven experience building APIs with FastAPI or Flask.
- Knowledge of secure AI deployment patterns, access controls, and AI safety guidelines.
- Understanding of embeddings, transformer models, and fine-tuning methods.
- Experience evaluating LLM outputs using BLEU, ROUGE, RAGAS, or LLM-as-judge methods.
- Familiarity with CI/CD, Docker, containerization, and cloud-native deployments.
- Exposure to MLOps tools such as Prefect, ADF, Airflow, or prompt testing frameworks.
- Strong debugging, analytical thinking, and ownership mindset.

Key Responsibilities:

- Design and develop GenAI and LLM-powered backend services using Python.
- Build RAG pipelines including ingestion, chunking, embedding, retrieval, and inference layers.
- Implement multi-step agent workflows,



tool-calling logic, and structured reasoning flows using LangChain or LangGraph.
- Integrate with Vertex AI or Azure OpenAI for model serving, vector search, and embeddings.
- Integrate vector databases (PGVector, MongoDB) for semantic retrieval.
- Develop APIs using FastAPI or Flask to expose LLM or GenAI capabilities as internal services.
- Apply secure deployment patterns, ensuring responsible use of LLMs and APIs.
- Write unit tests, evaluation scripts, and metrics-based quality assessments for LLM outputs.
- Containerize AI services using Docker and manage cloud deployments (Azure/GCP).
- Develop CI/CD workflows for LLM application updates, versioning, and automation.
- Collaborate with nearshore technical leads for requirement alignment, design reviews, and execution.
- Produce clear documentation for pipelines, models, prompts, workflows, and architecture decisions.

Domain Expertise:

- GenAI/LLM Development
- Python Backend Engineering
- Vector Search & Retrieval Systems
- Cloud AI Platforms (Vertex AI / Azure OpenAI)
- MLOps & AI Workflow Orchestration

Soft Skills:

- Excellent communication skills
- Strong analytical and debugging mindset
- Collaboration with distributed teams
- Documentation and knowledge sharing
- Ownership and accountability

Education Requirements:

- Optional

Certifications (Optional):

- Google Cloud ML or Vertex AI certifications
- Azure AI Engineer or Azure OpenAI certifications
- Python or MLOps certifications

📌 AI Engineer REG (Karnataka)
🏢 Sparix Global
📍 Karnataka