Role & responsibilities:

Design and build agentic AI systems - tool use, multi-agent orchestration, React/chain-of-thought pipelines.

- Develop and deploy LLM-powered features: RAG pipelines, autonomous workflow automation.
- Own prompt engineering and context engineering: manage context windows, token budgets, and output structuring.
- Build and maintain backend services using Python and Fast API; design and manage relational databases with SQL Alchemy and Alembic migrations.
- Host and serve custom models on AWS Sage Maker; manage endpoints, scaling, and inference pipelines.
- Work with open-source models via Hugging Face - model selection, inference, and lightweight customization.
- Set up and maintain ML/AI-Ops workflows - CI/CD pipelines for model deployment, automated testing, and continuous delivery of AI features. Stay updated with the latest advancements in ML, AI, deep learning, and agentic frameworks.

Preferred candidate profile: 4+ years of experience in Python (3.10+) clean, production-grade code with async patterns and FastAPI.





GenAI fundamentals: solid understanding of tokens, embeddings, prompt engineering, and context engineering.

Familiarity with Agent-to-Agent (A2A) and Model Context Protocol (MCP).

Basic ML/NLP knowledge: classification, regression, NLP pipelines, BERT, LIWC, Bag of Words, and embedding-based similarity models.

Hands-on experience deploying and hosting custom models on AWS Sage Maker.

Familiarity with the Hugging Face ecosystem and open-source model landscape.

Experience with ML/AIOps - CI/CD pipelines for model lifecycle, model versioning, monitoring, and automated evaluation in production.

Strong grasp of data structures, algorithms, and software engineering principles.

Excellent problem-solving skills with the ability to work independently and collaboratively in a rapid-paced environment.

Good written and verbal communication skills.

📌 Software Engineer AI (Delhi)
🏢 Smart Source
📍 Delhi