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AI Implementation Engineer

Wissen Technology Pvt. Ltd.

Bengaluru / Mumbai

6-12 years

1 day ago

$49.4K–78.3K/yr

Full-time

Hybrid

Skills Required LLM

RAG

LangChain

Vector Database

Embeddings

Prompt Engineering

Azure OpenAI Service

LLM Functions

LLM operations

Chunking

Reranking

Grounding Techniques

Azure AI Foundry

Azure AI Search

Microsoft Agent Framework

Description Wissen Technology is hiring an AI Implementation Engineer to build, deploy, and scale enterprise-grade Generative AI solutions. The role focuses on Azure AI services, LLMs, RAG, and agent-based frameworks in collaboration with engineering teams.

Company: Wissen Technology Pvt. Ltd.

Role: AI Implementation Engineer

Location: Bangalore/Mumbai | Hybrid

Experience

- 6 - 12 years
- Python programming: 6+ years
- Java and Spring Framework: 3+ years

Qualification

- MCA
- B.E
- B.Tech
- MTech
- BE/BTech/ME/MTech in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or related field

Responsibilities

- Build, deploy, and scale enterprise-grade Generative AI solutions
- Develop production-ready AI systems for business-critical applications
- Use Azure AI services, LLMs, RAG architecture, and agent-based frameworks
- Design and deploy enterprise GenAI solutions using Azure OpenAI, Azure AI Foundry, and Azure AI Search
- Build and maintain RAG pipelines, AI agents, APIs, and enterprise application integrations
- Collaborate closely with engineering delivery pods to accelerate AI adoption
- Provide hands-on technical guidance to engineering teams
- Implement production-grade AI capabilities including evaluation frameworks, observability, guardrails, caching, and CI/CD pipelines
- Integrate AI systems securely with enterprise data platforms
- Ensure 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

Additional Responsibilities

- Implement production-ready AI systems using Azure AI services, Large Language Models, RAG architecture, and agent-based frameworks
- Support enterprise-scale application integration with Java and Spring Framework
- Use Snowflake and Cortex AI with strong SQL expertise
- Apply prompt engineering, LLM evaluation frameworks, testing, and model performance optimization
- Use DevOps and cloud deployment tools including Azure DevOps, GitHub Actions, Docker, AKS, and Azure Functions
- Work with observability tools for AI systems
- Develop AI-powered user interfaces and conversational applications
- Implement Azure AI Content Safety and Responsible AI
- Work on financial services, banking, or other regulated industry domains
- Support real-time streaming applications and token-level LLM operations
- Apply performance optimization and cost optimization for AI workloads
- Mentor engineering teams and drive AI adoption across development squads
- Own end-to-end delivery from concept to production deployment
- Use solid analytical thinking to resolve complex AI implementation challenges
- Communicate effectively across technical and business teams
- Work within cross-functional product engineering teams and enable knowledge transfer

Nice 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
- Microsoft Azure AI Engineer Associate Certification




- Experience mentoring engineering teams and driving AI adoption across development squads
- Proven track record of delivering end-to-end AI solutions from concept to production deployment
- Strong analytical thinking with demonstrated ability to resolve complex AI implementation challenges
- Excellent written and verbal communication skills with the ability to collaborate across technical and business teams
- Experience implementing evaluation frameworks, guardrails, monitoring, and observability for AI systems
- Ability to work within cross-functional product engineering teams and enable knowledge transfer

More Skills Python, Asynchronous programming, Backend application development, Java, Spring Framework, Retrieval Augmented Generation (RAG), Semantic Kernel, AutoGen, Lang Graph, Snowflake, Cortex AI, Cortex Search, SQL, LLM evaluation frameworks, Testing, Model performance optimization, Azure DevOps, GitHub Actions, Docker, AKS, Azure Functions, Observability tools, React.js, Azure AI Content Safety, Responsible AI, Real-time streaming applications, Token-level LLM operations, Caching strategies, Cost optimization, Microsoft Azure AI Engineer Associate Certification

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