Technical AI Architect - Hybrid role

[chandansoni...@gmail.com]

Technical AI Architect - Hybrid role

Years of Experience: 12+ Years
Location: Nashville, TN
Mode: Hybrid. Working from office for 2 days a week.

Key Responsibilities
1. Architecture & System Design
• Own the end-to-end architecture of large-scale, distributed GenAI platforms, including microservices, data pipelines, and AI inference layers.
• Define reference architectures and design patterns for Generative AI, agentic workflows, and AI-enabled enterprise platforms.
• Ensure all systems are secure, scalable, fault-tolerant, cost-efficient, and production-ready.
2. Agentic Systems & Workflow Orchestration
• Design and implement autonomous and semi-autonomous multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom orchestration engines.
• Enable agent collaboration, task planning, memory management, tool use, and self-reflection capabilities.
• Architect agent-driven enterprise workflows (e.g., code generation, testing, incident triage, knowledge discovery, and business process automation).
3. Generative Model Engineering
• Lead model selection, fine-tuning, and optimization of Large Language Models (LLMs) and Small Language Models (SLMs), including OpenAI, Anthropic, Gemini, LLaMA, Mistral, and domain-specific models.
• Apply Parameter-Efficient Fine-Tuning (PEFT) techniques such as LoRA, QLoRA, adapters, and distillation to optimize cost and performance.
• Oversee Retrieval-Augmented Generation (RAG) architectures, vector search, prompt engineering, memory augmentation, and evaluation pipelines.
• Drive experimentation with Diffusion models, GANs, and multimodal models where applicable.
4. LLMOps / MLOps & Cloud Infrastructure
• Architect and standardize LLMOps/MLOps pipelines for training, evaluation, deployment, observability, and lifecycle management.
• Design cloud-native AI platforms on AWS, Azure, or GCP, leveraging GPU/TPU infrastructure, Kubernetes, and serverless computing patterns.
• Implement comprehensive monitoring for latency, hallucinations, model drift, cost usage, security events, and SLA compliance.
• Optimize inference using techniques such as quantization, batching, caching, and intelligent model routing.
5. AI-Driven SDLC & Developer Experience
• Architect AI-augmented Software Development Lifecycle (SDLC) systems, including:
• Agentic code generation and refactoring
• Automated test generation and validation
• Intelligent CI/CD workflows
• AI-powered documentation and knowledge management
• Partner with platform and Developer Experience (DevEx) teams to embed AI into developer tooling and workflows.
6. Governance, Security & Responsible AI
• Define AI governance frameworks covering model risk, data privacy, lineage, explainability, bias detection, and regulatory compliance.
• Ensure alignment with security, legal, and regulatory requirements (e.g., HIPAA, SOC2, GDPR, as applicable).
• Establish robust guardrails for safe agent behavior, access control, prompt injection defense, and data leakage prevention.
7. Strategy, Leadership & Collaboration
• Serve as a technical thought leader and advisor to executive stakeholders.
• Lead and mentor senior engineers, data scientists, and AI researchers.
• Manage multiple concurrent initiatives while balancing innovation with reliable delivery.
• Drive buy-vs-build decisions, vendor evaluations, and strategic roadmap planning.
• Evangelize AI best practices across engineering, product, and data teams.

Required Qualifications
Core Engineering & Architecture
• 12+ years of experience in enterprise-grade full-stack or platform architecture.
• Strong background in product engineering, distributed systems, and microservices.
• Demonstrated ability to design mission-critical, high-availability systems.
AI / ML & Generative AI Expertise
• Strong theoretical and hands-on expertise in:
• Deep Learning (CNN, RNN, LSTM)
• Transformer architectures and attention mechanisms
• Deep experience with Generative AI, including:
• Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering
• GANs and Diffusion models
• Proven experience integrating with OpenAI, Azure OpenAI, Hugging Face, or equivalent platforms.

Technical Stack
• Expert-level proficiency in Python; strong working knowledge of C++ and Java.
• Extensive experience with PyTorch, TensorFlow, and Keras.
• Expertise in designing RESTful APIs, GraphQL, and event-driven architectures using Kafka or RabbitMQ.
• Strong understanding of databases, vector stores, and streaming systems.
Cloud & DevOps
• Proven track record of deploying and operating large-scale ML/AI workloads in production.
• Hands-on experience with Kubernetes, Docker, and Infrastructure as Code (IaC) tools (Terraform, Bicep, or CloudFormation).
• Familiarity with CI/CD pipelines, observability stacks, and secure cloud networking.
Preferred Other Skills
• Experience in Healthcare, Payer, or Life Sciences domains, including regulated data environments.
• Exposure to edge AI, on-device inference, or real-time decision-making systems.
• Contributions to open-source AI/ML projects or published technical thought leadership.
• Experience building internal AI platforms or AI Centers of Excellence (CoE).

What Success Looks Like
• Enterprise-scale Generative AI platforms run reliably and efficiently in production.
• Autonomous agents delivering measurable productivity gains across the organization.
• Secure, governable, and cost-efficient AI ecosystems.
• Engineering teams are empowered by AI-native tooling and workflows.
• Clear architectural vision consistently aligns with strategic business outcomes.