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 Youll Work On

You will be part of Wissen Technologys 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