AI Implementation Strategist

Wissen Technology is hiring a Senior AI Engineering Specialist to drive the architecture, design, and

implementation of enterprise-scale Generative AI solutions. This is a hands-on Principal Engineer /

Architect-level role within the Wissen GenAI Practice focused on identifying high-value AI use cases, defining reusable AI patterns, mentoring implementation teams, and delivering production-grade AI applications on Microsoft Azure. The role combines strategic AI leadership with hands-on engineering, enabling global delivery teams to accelerate product development, improve engineering productivity, and build secure, governed,

and scalable AI-powered solutions.

Experience: 6-12 years

Location: Mumbai

Mode of Work: Hybrid

Mandatory Skills (Must Have)

• Generative AI & LLM Engineering (3+ years) building and deploying production-grade GenAI

applications, AI assistants, AI agents, and enterprise AI solutions

• Microsoft Azure AI Services including Azure AI Foundry, Azure OpenAI Service, Prompt Flow,

Azure AI Search, Azure AI Document Intelligence, and Azure Machine Learning

• Enterprise RAG Development including embeddings, chunking strategies, vector search, hybrid

retrieval, semantic ranking, reranking, grounding, and evaluation frameworks

• Agentic AI Frameworks such as Microsoft Agent Framework, Semantic Kernel, AutoGen,

LangChain, or LangGraph with tool/function calling and multi-agent orchestration

• Python Development (5+ years) including asynchronous programming, API development, AI

workflow implementation, and scalable backend engineering

• Solution Architecture & System Design involving enterprise integrations, API-first architecture,

microservices, data access layers, and cloud-native application development

• LLMOps & AI Platform Engineering including observability, evaluation frameworks, prompt

lifecycle management, CI/CD pipelines, model monitoring, cost optimization, and performance tuning

• Responsible AI & Governance including Azure AI Content Safety, guardrails, prompt injection

prevention, access controls, auditability, and enterprise AI risk management

Optional Skills (Good to Have)

• Snowflake and Cortex AI including Cortex Search, LLM Functions, AI-driven analytics, and

enterprise data grounding

• Microsoft Fabric, Azure Databricks, Data Lake architectures, and advanced analytics platforms

• Azure DevOps, GitHub Actions, Infrastructure as Code, CI/CD automation, and DevSecOps

practices

• Docker, Kubernetes (AKS), Azure Functions, Azure App Services, and modern cloud-native

deployment architectures

• Financial Services, Banking, Capital Markets, Insurance, or other regulated industry experience

• Fine-tuning, model distillation, model routing, evaluation-driven development, and advanced

prompt engineering

• Enterprise Java/JVM application architecture and AI integration within large-scale application

ecosystems

• Azure AI Engineer Associate and Azure Solutions Architect Expert certifications

Professional Attributes & Qualifications

• Education: BE/BTech/ME/MTech in Computer Science, Information Technology, Artificial

Intelligence, Data Science, Engineering, or a related field

• Leadership: Proven experience leading technical initiatives, mentoring engineers, conducting

architecture reviews, and driving GenAI adoption across multiple teams

• Ownership: Demonstrated track record of owning AI initiatives from use-case definition through

architecture, development, deployment, and production support

• Strategic Thinking: Ability to evaluate and prioritize AI use cases based on business value,

implementation feasibility, compliance requirements, and ROI

• Problem Solving: Strong analytical, troubleshooting, debugging, and optimization capabilities

within large-scale enterprise environments

• Collaboration: Experience working closely with architects, engineering leaders, product

managers, business stakeholders, and governance teams

• Communication: Strong written and verbal communication skills with the ability to explain complex AI concepts to both technical and non-technical audiences

• Quality Focus: Commitment to engineering excellence, secure software development practices,

governance standards, code quality, testing automation, and responsible AI implementation

Key Responsibilities

• Partner with business stakeholders and engineering teams to identify, evaluate, and prioritize

high-impact Generative AI use cases

• Define and evolve reusable Azure-based AI reference architectures and implementation

frameworks for enterprise-wide adoption

• Design and implement end-to-end GenAI solutions leveraging Azure OpenAI, Azure AI Foundry,

Azure AI Search, Prompt Flow, and enterprise data sources

• Architect advanced RAG systems and AI agent workflows with strong focus on grounding, retrieval optimization, accuracy, scalability, and governance

• Establish AI engineering best practices covering prompt engineering, evaluations, observability,

content safety, security controls, and deployment standards

• Work closely with implementation engineers and delivery pods to mentor teams, conduct technical reviews, remove adoption blockers, and transfer knowledge

• Integrate AI capabilities with enterprise applications, APIs, databases, and business workflows

while adhering to security and compliance requirements

• Implement AI monitoring, evaluation frameworks, performance measurements, cost optimization

strategies, and measurable business-impact reporting

• Collaborate with governance teams to ensure enterprise AI solutions comply with responsible AI

principles, regulatory standards, and organizational policies

• Contribute reusable frameworks, accelerators, guardrails, and engineering patterns that improve

productivity and speed of AI adoption across the organization

What You'll Work On

You will be part of Wissen Technology's GenAI Practice working on enterprise-scale AI

transformation programs across global clients. This role focuses on both greenfield AI initiatives and modernization programs where Generative AI capabilities are embedded directly into

business-critical applications.

You will work with Azure OpenAI, Azure AI Foundry, Azure AI Search, Semantic Kernel, Microsoft

Agent Framework, Snowflake Cortex AI, modern cloud-native platforms, and enterprise data

ecosystems. Your work will help organizations accelerate software delivery, improve developer

productivity, automate business operations, and create secure, scalable AI-powered products.

What Success Looks Like

• A prioritized and business-aligned GenAI use-case portfolio adopted by business and engineering stakeholders

• Two to three flagship Generative AI solutions successfully deployed to production with measurable business impact

• Reusable AI reference architectures, governance frameworks, and implementation accelerators

adopted across delivery teams

• Significant contribution toward improved engineering productivity, faster time-to-market, and

enhanced software quality

• Increased GenAI maturity across engineering organizations through mentoring, enablement, and

adoption of AI best practices