GEN AI Senior Architect

About Tredence:

Tredence is a global data science solutions provider founded in 2013 byShub Bhowmick, Sumit Mehra, and Shashank Dubey focused on solving the last-mile problem in AI. Headquartered in San Jose, California, the company embraces a vertical-first approach and an outcome-driven mindset to help clients win and accelerate value realization from their analytics investments. The aim is to bridge the gap between insight delivery and value realization by providing customers with a differentiated approach to data and analytics through tailor-made solutions. Tredence is 4500-plus employees strong with offices in San Jose, Foster City, Chicago, London, Toronto, and Bangalore, with the largest companies in retail, CPG, hi-tech, telecom, healthcare, travel, and industrials as clients.

About the Role

We are looking for an experienced and visionarySeniorGenerative AI Architect with 12+ years of experience in AI/ML, including hands-on work withLLMs (Large Language Models) andGenerative AI solutions. In this strategic technical leadership role, you will be responsible for designing and overseeing the development of advanced GenAI platforms and solutions that transform business operations and customer experiences.

As the GenAI Architect, you will work closely with data scientists, ML engineers, product teams, and stakeholders to conceptualize, prototype, and scale generative AI use cases across the organization or client engagements.

Key Responsibilities

GenAI Solution Architecture & Design

·Lead the design and development of scalableGenAI solutions leveragingLLMs,diffusion models, andmultimodal architectures.

·Architect end-to-end pipelines involvingprompt engineering,vector databases,retrieval-augmented generation (RAG), andLLM fine-tuning.

·Select and integrate foundational models (e.g.,GPT, Claude, LLaMA, Mistral) based on business needs and technical constraints.

Technical Strategy & Leadership

·DefineGenAI architecture blueprints, best practices, and reusable components for rapid development and experimentation.

·Guide teams onmodel evaluation, inference optimization, andcost-effective scaling strategies.

·Stay current on the rapidly evolving GenAI landscape and assess emerging tools, APIs, and frameworks.

Collaboration & Delivery

·Work with product owners, business leaders, and data teams to identifyhigh-impact GenAI use cases across domains like customer support, content generation, document understanding, and code generation.

·SupportPoCs, pilots, and production deployments of GenAI models in secure, compliant environments.

·Collaborate with MLOps and cloud teams to enablecontinuous delivery, monitoring, and governance of GenAI systems.

Required Qualifications & Experience

·Education: Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or related technical field. PhD is a plus.

·Experience: 12–15 years in AI/ML and software engineering, with3+ years focused on Generative AI and LLM-based architectures.

Core Skills

·Deep expertise inmachine learning,natural language processing (NLP), anddeep learning architecture.

·Hands-on experience withLLMs, transformers, fine-tuning techniques (LoRA, PEFT), andprompt engineering.

·Proficient inPython, with libraries/frameworks such asHugging Face Transformers, LangChain, OpenAI API, PyTorch, TensorFlow.

·Experience withvector databases (e.g., Pinecone, FAISS, Weaviate) andRAG pipelines.

·Strong understanding ofcloud-native AI architectures (AWS/GCP/Azure), containerization (Docker/Kubernetes), and API integration.

Architectural & Leadership Skills

·Proven ability to design and deliverscalable, secure, and efficient GenAI systems.

·Strong communication skills for cross-functional collaboration and stakeholder engagement.a

·Ability to mentor engineering teams and drive innovation across the AI/ML ecosystem.

Nice-to-Have

·Experience withmultimodal models (text + image/audio/video).

·Knowledge ofAI governance, ethical AI, and compliance frameworks.

·Familiarity withMLOps practices for GenAI, including model versioning, drift detection, and performance monitoring.