AI Implementation Strategist

Wissen Technology; Mumbai; Hybrid; INR 1,000,000 - 1,500,000; Full time

Job summary

Wissen Technology in Mumbai seeks a Senior AI Engineering Specialist to drive architecture and implementation of enterprise-scale Generative AI solutions. This hands-on Principal Engineer role anchors Wissen GenAI Practice, mentoring teams and delivering production-grade AI apps on Microsoft Azure.

Hybrid work model supports global delivery with a focus on high-value use cases, reusable patterns, governance, and secure, scalable GenAI platforms across the organization.

Qualifications

• GenAI & LLM engineering for production-grade GenAI apps and AI agents.
• Agentic AI frameworks for multi-agent orchestration and tool calling.
• Python development with async, API dev, scalable backend.
• Enterprise integrations, API-first, microservices, data access layers.
• LLMOps including observability, prompt lifecycle, CI/CD, model monitoring.

Responsibilities

• Identify and prioritize high-impact GenAI use cases.
• Define reusable AI reference architectures for Azure-based deployment.
• Design GenAI solutions with Azure OpenAI, AI Search, and data sources.
• Architect RAG systems with grounding, retrieval optimization, and governance.
• Mentor delivery teams, conduct reviews, remove blockers, share knowledge.
• Ensure security, compliance, and governance in AI projects.
• Collaborate with governance teams to ensure responsible AI practices.

Skills

GenAI Engineering

Agentic AI Frameworks

Python Development

Solution Architecture

LLMOps & AI Platform

Education

BE/BE/ME/ME/MTech in CS/AI

Tools

Microsoft Agent Framework

Semantic Kernel

AutoGen

LangChain

LangGraph

Job description

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

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 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 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
• 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
• Financial Services, Banking, Capital Markets, Insurance, or other regulated industry experience
• Fine‑tuning, model distillation, model routing, evaluation‑driven development, and advanced
• 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