Responsibilities

- Design and build AI-powered features using existing GenAI models (OpenAI, Anthropic,
Gemini, etc.) via their APIs for text, image, and multimodal tasks.
- Fine-tune or adapt lightweight open-source models (e.g., using LoRA/QLoRA, PEFT) for
domain-specific use cases when off-the-shelf models fall short.
- Build robust prompt engineering pipelines, including prompt design, evaluation, and iteration
for consistent, reliable outputs.
- Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases
(pgvector, Pinecone, Weaviate, etc.) to ground model outputs in proprietary data.
- Design and orchestrate multi-step AI workflows/agents (tool use, function calling, chaining) for
complex tasks.
- Manage the end-to-end lifecycle of AI features: data preparation, model/API selection,
evaluation, integration, and deployment.
- Collaborate with backend/frontend engineers and product managers to integrate AI
capabilities into existing systems and products.
- Monitor and optimize model performance, latency, and cost (token usage, API costs,
inference costs for self-hosted models).
- Stay current with the rapid-evolving GenAI landscape (new model releases, APIs, techniques)
and evaluate their applicability to our product.
- Ensure ethical AI practices fairness, transparency, data privacy, and responsible handling




of user data sent to third-party model APIs

Requirements

- Bachelor s or Master s degree in Computer Science, AI/ML, or a related field (or equivalent
practical experience).
- 2 3 years of hands-on experience building applications using GenAI/LLM APIs (OpenAI,
Anthropic, Google, etc.).
- Strong Python skills, with experience calling and orchestrating AI APIs in production systems.
- Practical experience with prompt engineering, structured outputs, and function/tool calling.
- Experience fine-tuning or adapting pre-trained/open-source models (Hugging Face
ecosystem, LoRA/QLoRA/PEFT) for specific tasks.
- Experience building RAG pipelines embeddings, vector search, chunking strategies,
retrieval evaluation.
- Familiarity with agent frameworks/orchestration (LangChain, LangGraph, LlamaIndex, or
similar).
- Understanding of transformer architecture fundamentals enough to reason about model
behaviour, limitations, and fine-tuning trade-offs (deep training-from-scratch expertise not
required).

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Engineer (GenAI Applications) (Gujarat)
🏢 iSummation
📍 Gujarat