Solution Architect Artificial Intelligence (AI) & Generative AI Practice

Job Description: Solution Architect – Artificial Intelligence (AI) & Generative AI Practice

Company: Teceze Ltd

Position: Solution Architect (SA) – AI & Generative AI

Practice Area: Digital Engineering & Data Intelligence

Location: Chennai & Bangalore

Employment Type: Full-time

About Tecez

eTeceze is a global technology partner focused on operational resilience, digital transformation, and scalable growth. We simplify architectural complexity and accelerate business velocity by uniting next-generation innovation under a highly accountable delivery model

.Our AI & Intelligent Automation Engine sits at the leading edge of enterprise innovation. We construct production-grade, highly secure computing and data structures that allow major enterprise clients to seamlessly integrate Generative AI, Large Language Models (LLMs), Agentic workflows, and Advanced Data Pipelines directly into their core operational workflows—turning unstructured data debt into measurable business velocity

.
Position Overvi

ewWe are seeking an exceptionally skilled, forward-thinking Solution Architect for our AI & Generative AI Practice. In this role, you will be the primary technical strategist responsible for designing and productionizing end-to-end enterprise AI system

s.You will operate at the critical intersection of business strategy and high-velocity engineering. You will collaborate directly with client CXOs to identify high-value AI opportunities, while simultaneously writing structural blueprints for our machine learning and software engineering squads. Your mission is to move AI from proof-of-concept into highly resilient, scalable, cost-optimized, and ethically compliant production environment

s.Key Responsibiliti

• esAI Architecture Blueprinting: Lead the structural discovery, scoping, and high/low-level design (HLD/LLD) of enterprise AI solutions, including Retrieval-Augmented Generation (RAG) frameworks, multi-agent orchestrations, and fine-tuning environment
• s.Infrastructure Design for AI: Architect highly scalable compute infrastructure tailored for heavy AI workloads across public hyperscalers (AWS, Azure, GCP). Design optimized data ingestion pipelines, vector database strategies (e.g., Pinecone, Milvus, Qdrant), and model deployment framework
• s.LLM Operations (LLMOps) & Engineering: Establish robust LLMOps and MLOps pipelines to govern model lifecycle management, continuous evaluation, prompt version control, model monitoring, and guardrail enforcement (e.g., handling hallucinations, data leakage, and alignment
• ).Data Strategy Alignment: Partner with data engineering teams to design high-throughput data processing architectures capable of preparing, cleaning, and structuring enterprise data for training and inference layer
• s.Pre-Sales & Enterprise Advisory: Assist our business development teams during early-stage client consultations. Lead technical AI workshops, evaluate existing legacy infrastructures, write formal Statements of Work (SOWs), and demonstrate clear ROI regarding AI tokens, infrastructure costs, and business valu
• e.Governance & Ethics Enforcement: Build enterprise-grade security structures around AI layers, ensuring compliance with strict regional data residency rules (Middle East and India), robust identity access management (IAM), and responsible AI practice

s.
Requirements & Qualificati

onsExperience & Educat

• ion8 to 12+ years of professional experience in software engineering, data systems, or cloud architecture, with at least 3+ years spent designing and deploying production-level AI/ML solutio
• ns.A proven track record of moving Generative AI workloads (such as enterprise search, automated document intelligence, or conversational agents) out of local sandboxes and into scaled corporate producti
• on.Strong conceptual command over foundational transformer architectures, neural networks, and prompt engineering paradig

ms.Technical Domain Expert

• iseGenAI Ecosystem frameworks: Practical, production-level engineering mastery using frameworks like LangChain, LlamaIndex, or AutoG
• en.Model Providers: Hands-on architectural command over major model integrations via API or self-hosting (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, HuggingFace, Llama open-source model
• s).Vector Datasets & Caching: Advanced proficiency with vector storage options, embedding models, and caching mechanisms to manage latency and cost optimizatio
• ns.Programming & MLOps: Mastery of Python and its core data science stack (PyTorch, TensorFlow, Pandas). Experience with MLOps/LLMOps tracking tools (e.g., MLflow, Weights & Biases, LangSmit
• h).Cloud Infrastructure: Strong command of core containerization (Docker, Kubernetes) and hyperscaler AI tools (SageMaker, Azure AI Studio, Vertex A