Ai Ml Engineer

Wintech Services; Hyderabad; On-site; INR 2,500,000 - 6,000,000; Full time

Benefits offered by this job

Health insurance

Job summary

Wintech Services in Hyderabad seeks an experienced GenAI/ML Engineer to architect and deploy scalable AI solutions across the full project lifecycle. You will design enterprise-grade conversational AI platforms, including RAG-based systems, and leverage Azure OpenAI services.

You will apply prompt engineering, embeddings, vector search, and retrieval strategies while ensuring governance, security, and production readiness. Collaboration with product and engineering teams is essential.

Qualifications

• 3 years of progressive experience in software engineering, data, or big data related roles.
• 2+ years of hands-on experience in AI/ML-focused roles, with deep expertise in applied machine learning.
• Advanced knowledge and practical experience in machine learning and natural language processing (NLP), particularly large language models (LLMs).
• Proven experience building and deploying LLM-based applications, including prompt engineering, context management, embeddings, and retrieval strategies.
• Demonstrated success delivering enterprise-scale chatbots, conversational AI systems, or virtual assistants.
• Strong experience with CI/CD pipelines, containerization technologies (Docker, Kubernetes), and ML lifecycle tooling such as MLflow or equivalent.
• Solid understanding of AI governance, compliance, bias mitigation, explainability, and responsible AI practices.
• Strong knowledge of cloud security, identity management (Azure AD/Entra ID), data governance, and enterprise risk controls.
• Experience leading or significantly contributing to large-scale AI modernization or transformation initiatives.
• Proficiency in Python and modern AI development frameworks for building scalable, production-ready AI systems.

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 retraining, 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.
• Build and maintain CI/CD pipelines, monitoring, and lifecycle management for AI and LLM workloads in production environments.
• 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).
• Define success metrics aligned with business objectives and continuously evaluate, measure, and improve model quality and system performance.
• Establish design standards, reference architectures, and best practices for enterprise AI systems.
• Ensure AI solutions meet enterprise requirements for security, reliability, scalability, governance, and compliance.
• Evaluate, prototype, and adopt emerging AI patterns, frameworks, and Azure capabilities.
• Maintain familiarity with frontend integrations for chat interfaces across web applications, Microsoft Teams, and Copilot.
• Serve as a technical mentor for senior and junior engineers, setting a high bar for engineering excellence.
• Partner closely with product leaders, architects, and business stakeholders to translate business requirements into scalable AI solutions.
• Communicate complex AI concepts and architectural decisions effectively to both technical and non-technical stakeholders.

Skills

Python

LLMs

NLP

Conversational AI

Prompt engineering

Embeddings

Retrieval

CI/CD

Docker

Kubernetes

AI governance

Cloud security

Tools

MLflow

Azure OpenAI

Azure AI Foundry

Vector search

Job description

Required Qualifications

• 3 years of progressive experience in software engineering, data, or big datarelated roles.
  
• 2+ years of hands-on experience in AI/ML-focused roles, with deep expertise in applied machine learning.
  
• Advanced knowledge and practical experience in machine learning and natural language processing (NLP), particularly large language models (LLMs), including their integration, optimization, and evaluation.
  
• Proven experience building and deploying LLM-based applications, including prompt engineering, context management, embeddings, and retrieval strategies.
  
• Demonstrated success delivering enterprise-scale chatbots, conversational AI systems, or virtual assistants.
  
• Strong experience with CI/CD pipelines, containerization technologies (Docker, Kubernetes), and ML lifecycle tooling such as MLflow or equivalent.
  
• Solid understanding of AI governance, compliance, bias mitigation, explainability, and responsible AI practices.
  
• Strong knowledge of cloud security, identity management (Azure AD/Entra ID), data governance, and enterprise risk controls.
  
• Experience leading or significantly contributing to large-scale AI modernization or transformation initiatives.
  
• Proficiency in Python and modern AI development frameworks for building scalable, production-ready AI systems.
  

Key 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 retraining, 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.
  
• Build and maintain CI/CD pipelines, monitoring, and lifecycle management for AI and LLM workloads in production environments.
  
• 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).
  
• Define success metrics aligned with business objectives and continuously evaluate, measure, and improve model quality and system performance.
  
• Establish design standards, reference architectures, and best practices for enterprise AI systems.
  
• Ensure AI solutions meet enterprise requirements for security, reliability, scalability, governance, and compliance.
  
• Evaluate, prototype, and adopt emerging AI patterns, frameworks, and Azure capabilities.
  
• Maintain familiarity with frontend integrations for chat interfaces across web applications, Microsoft Teams, and Copilot.
  
• Serve as a technical mentor for senior and junior engineers, setting a high bar for engineering excellence.
  
• Partner closely with product leaders, architects, and business stakeholders to translate business requirements into scalable AI solutions.
  
• Communicate complex AI concepts and architectural decisions effectively to both technical and non-technical stakeholders.