AI Implementation Engineer

Wissen Technology; Bengaluru South; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Wissen Technology is hiring an AI Implementation Engineer to design, build, deploy, and scale enterprise-grade Generative AI solutions across critical applications. You will work with Azure AI services, LLMs, RAG, and agent frameworks, partnering with engineering teams to accelerate AI adoption and innovation.

Ideal candidates have strong Python development experience, 6+ years, plus Java/Spring for integration, and familiarity with Azure OpenAI services. Hybrid work model offered.

Qualifications

• 6+ years Python programming with backend development
• 3+ years Java/Spring for enterprise integrations
• Experience with Azure OpenAI, Azure AI Foundry, and Azure AI Search
• Knowledge of RAG architecture, embeddings, vector databases, and grounding techniques
• Familiarity with DevOps and cloud deployment: Azure DevOps, Docker, AKS, Azure Functions
• Understanding of performance optimization, cost control for AI workloads

Responsibilities

• Build production-ready GenAI solutions using Azure AI services
• Deploy and scale enterprise-grade AI systems across applications
• Collaborate with engineering teams to drive AI adoption
• Develop RAG pipelines with embeddings, vector stores and grounding
• Implement CI/CD, observability, and monitoring for AI workloads
• Mentor teams and drive AI delivery best practices

Skills

Python programming

Java

Azure OpenAI

LLM

Azure DevOps

Docker

AKS

SQL

Prompt engineering

Education

MCA/B.E/B.Tech/MTech

Tools

Azure OpenAI Service

Azure AI Foundry

Azure AI Search

Snowflake

Cortex AI

LangChain

Job description

Bangalore South, India | Posted on 09/10/2026

Wissen Technology is hiring for AI Implementation Engineer

At Wissen Technology, we deliver niche, custom-built products that solve complex business challenges across industries worldwide. Founded in 2015, our core philosophy is built around a strong product engineering mindset—ensuring every solution is architected and delivered right the first time. Today, Wissen Technology has a global footprint with 2000+ employees across offices in the US, UK, UAE, India, and Australia. Our commitment to excellence translates into delivering 2X impact compared to traditional service providers. How do we achieve this? Through a combination of deep domain knowledge, cutting-edge technology expertise, and a relentless focus on quality. We don’t just meet expectations—we exceed them by ensuring faster time-to-market, reduced rework, and greater alignment with client objectives. We have a proven track record of building mission-critical systems across industries, including financial services, healthcare, retail, manufacturing, and more. Wissen stands apart through its unique delivery models. Our outcome-based projects ensure predictable costs and timelines, while our agile pods provide clients with the flexibility to adapt to their evolving business needs. Wissen leverages its thought leadership and technology prowess to drive superior business outcomes. Our success is powered by top-tier talent. Our mission is clear: to be the partner of choice for building world-class custom products that deliver exceptional impact—the first time, every time.

Job Summary:

Weare hiring an AI Implementation Engineer to build, deploy, and scale enterprise grade Generative AI solutions across business-critical applications. The role involves developing production-ready AI systems using Azure AI services, Large Language Models (LLMs), RAG architecture, and agent-based frameworks while collaborating closely with engineering teams to drive AI adoption and innovation.

• Experience: 6 - 12Years
  
• Mode of Work: Hybrid
  
• Education Qualification: MCA/B.E/B.Tech/MTech
  

Key Responsibilities & Duties:

• Python programming (6+ years) including asynchronous programming and backend application development
  
• Java and Spring Framework (3+ years) for enterprise-scale application integration
  
• Azure OpenAI Service, Azure AI Foundry, and Azure AI Search for production GenAI applications
  
• Retrieval Augmented Generation (RAG) architecture including embeddings, chunking, vector databases, reranking, and grounding techniques
  
• Agent Frameworks such as Microsoft Agent Framework, Semantic Kernel, AutoGen, Lang Chain, or Lang Graph
  
• Snowflake and Cortex AI (Cortex Search, LLM Functions) with strong SQL expertise
  
• Prompt Engineering, LLM evaluation frameworks, testing, and model performance optimization
  
• DevOps and Cloud Deployment using Azure DevOps, GitHub Actions, Docker, AKS, Azure Functions, and observability tools
  

Good to Have:

• React.js for AI-powered user interfaces and conversational applications
  
• Azure AI Content Safety and Responsible AI implementation experience
  
• Financial Services, Banking, or other regulated industry domain experience
  
• Real-time streaming applications and token-level LLM operations
  
• Performance optimization, caching strategies, and cost optimization for AI workloads
  

Nice to Have:

• Education: BE/BTech/ME/MTech in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or related field
  
• Leadership: Experience mentoring engineering teams and driving AI adoption across development squads
  
• Ownership: Proven track record of delivering end-to-end AI solutions from concept to production deployment
  
• Problem-Solving: Strong analytical thinking with demonstrated ability to resolve complex AI implementation challenges
  
• Communication: Excellent written and verbal communication skills with the ability to collaborate across technical and business teams
  
• Quality Focus: Experience implementing evaluation frameworks, guardrails, monitoring, and observability for AI systems
  
• Collaboration: Ability to work within cross-functional product engineering teams and enable knowledge transfer
  

Preferred Experience:

• Design, develop, and deploy enterprise-grade Generative AI solutions using Azure OpenAI, Azure AI Foundry, and Azure AI Search
  
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines, AI agents, APIs, and enterprise application integrations
  
• Collaborate closely with engineering delivery pods to accelerate AI adoption and provide hands-on technical guidance
  
• Implement production-grade AI capabilities including evaluation frameworks, observability, guardrails, caching, and CI/CD pipelines
  
• Integrate AI systems securely with enterprise data platforms while ensuring compliance, access control, and secrets management
  
• Optimize AI applications for grounding accuracy, latency, scalability, reliability, and operational cost
  
• Build and maintain agent-based solutions using Semantic Kernel, AutoGen, LangChain, LangGraph, or Microsoft Agent Framework
  
• Define, monitor, and improve quality metrics, model evaluations, and program-level AI delivery KPIs