Role & responsibilities

Senior AI Engineer (LLM, Agentic Workflows & AI Systems Design)Build intelligent AI systems using LLMs, data, and workflow automation. Design agent-driven applications grounded in explicit specifications and real-world feedback . Follow few core principles: spec-driven development (define before build), agentic design (multi-step, tool-using systems), and continuous improvement through data and feedback. This role is hands-onfocused on building production-ready systems and improving them over time using real usage signals.

Key Responsibilities

- Build LLM-powered workflows for automation, screening, and decision-support use cases
- Translate business problems into structured AI specifications (prompts, workflows, expected outputs, evaluation criteria)
- Design agent-like systems that leverage tools, APIs, and multi-step reasoning with human checkpoints
- Integrate data sources to enrich workflows and improve decision quality
- Implement feedback loops to continuously improve system performance
- Develop pipelines for logging, tracing, and analysing AI outputs
- Evaluate system performance using test cases, real data, and user feedback
- Continuously refine prompts, workflows, and system logic based on observed behaviour

Skills & Technologies

Programming & Backend: Python, FastAPI, Flask, REST APIs, GraphQL

LLM & GenAI:



Azure OpenAI, Anthropic API, Hugging Face Transformers, function calling, structured outputs

Agentic Frameworks: LangChain, LangGraph, AutoGen, CrewAI

Workflow & Orchestration: n8n, Temporal

Data & Storage: PostgreSQL, MySQL, MongoDB, Redis, Pinecone, Weaviate, FAISS, Chroma

Observability & Evaluation: LangSmith, Weights & Biases (W&B;), Helicone, MLflow

Voice & Integration (Optional): Twilio, Whisper, ElevenLabs

What We’re Looking For

- Strong programming skills, preferably in Python
- Experience building backend systems, APIs, and integrations
- Hands-on experience with LLMs / GenAI-based applications
- Solid understanding of prompt engineering and structured outputs
- Ability to work with data to analyse and improve AI system behaviour
- Exposure to spec-driven or design-first development approaches
- Familiarity with agentic patterns (multi-step workflows, tool usage, state handling)
- Basic understanding of evaluation approaches (test cases, scoring, feedback loops)

Experience

- 4–8 years of experience in software engineering, ML engineering, or related roles
- Strong background in backend systems, APIs, or data-driven applications
- Experience working on at least one LLM/GenAI-based implementation

📌 Senior Artificial Intelligence Engineer (Tamil Nadu)
🏢 Sage IT
📍 Tamil Nadu