AI Engineer

Wintech Services; Hyderabad; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Wintech Services in Hyderabad seeks an Architect to design and deliver scalable GenAI and ML solutions across the full project lifecycle, from concept to production.

You will build enterprise-grade conversational AI platforms using Azure OpenAI, Azure AI Foundry, and related services, applying prompt engineering, embeddings, and RAG techniques.

The role emphasizes automated retraining, model monitoring, and grounded outputs with MCP and agent SDKs to stay at the forefront of AI orchestration.

Qualifications

• Experience designing scalable GenAI/ML solutions.
• Strong background in prompt engineering and vector-based retrieval.
• Familiarity with modern AI orchestration concepts.

Responsibilities

• Architect, design, and deliver scalable GenAI and ML solutions across the full project lifecycle.
• Design and build enterprise-grade conversational AI platforms with RAG-based systems and agentic workflows.
• Apply prompt engineering, embeddings, vector search, and fine-tuning strategies to optimize LLM outputs.
• Implement retrieval-augmented generation (RAG) to ensure outputs are accurate and well-grounded.

Job description

Role & responsibilities

Architect, design, and deliver scalable GenAI and ML solutions across the full project lifecycle, from concept and experimentation to production deployment and optimization.

• Design and build enterprise-grade conversational AI platforms, including RAG-based systems and agentic workflows, leveraging Azure OpenAI, Azure AI Foundry, and Azure AI services.
• Apply advanced prompt engineering, embeddings, vector search, fine-tuning strategies, and tool/function calling to optimize LLM-driven solutions.
• Design and implement AI/ML models with feedback loops, automated re-training, and fine-tuning pipelines to continuously improve accuracy and relevance.
• Implement retrieval-augmented generation (RAG) techniques to ensure AI outputs are accurate, contextual, and grounded in approved data sources.

Stay at the forefront of LLM and agentic AI advancements, incorporating modern techniques such as few-shot learning, structured outputs, Model Context Protocol (MCP), and AI orchestration frameworks (e.g., Agent SDKs).