Full-Stack & AI Tech Lead - Onsite

NTT DATA•Houston, TX

32d•Onsite

About The Position

We are seeking a seasoned Full Stack Developer / Engineer with 10+ years of demonstrable experience to lead greenfield and modernization application development projects. The ideal candidate will be a hands-on technical leader who can design architecture, implement scalable solutions across frontend and backend, integrate AI capabilities, mentor engineers, and drive delivery using modern DevOps and Agile practices. This role requires strong technical depth, excellent communication, stakeholder management, and practical experience integrating AI tools and models into production applications.

Requirements

Minimum 8+ years of professional software development experience with significant time in full stack roles.
Proven experience leading application development projects and delivering production-grade systems.
Strong programming skills in at least one backend language: Java, C#, Python, Node.js (TypeScript/JavaScript), or Go.
Strong frontend experience: React, Angular, or Vue.js and modern CSS frameworks.
Solid knowledge of RESTful APIs, WebSockets/gRPC, and API design best practices.
Experience with relational and NoSQL databases: PostgreSQL, MySQL, MongoDB, Redis (or equivalent).
Operational experience with Cloud platforms: AWS, Azure, or GCP — including compute, managed databases, IAM, and observability services.
CI/CD and automation experience: Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
Containerization and orchestration: Docker, Kubernetes experience preferred.
Strong understanding of software architecture patterns, scalability, performance tuning, and security best practices.
Excellent communication, stakeholder management, and leadership skills.
Practical experience integrating AI/LLM services into applications (e.g., OpenAI, Azure OpenAI, Anthropic, Cohere).
Familiarity with prompt engineering, prompt templates, and managing prompt life-cycle.
Experience building Retrieval-Augmented Generation (RAG) workflows using vector/semantic search and embedding models.
Hands-on with vector databases: Pinecone, Milvus, Weaviate, or self-managed FAISS.
Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks for chaining prompts, tools, and memory.
Knowledge of LLM deployment/finetuning pipelines (fine-tuning, instruction tuning) and model management approaches.
Experience with MLops and monitoring for AI features: model versioning, drift detection, inference latency, cost controls.
Awareness of AI safety, privacy, and ethical considerations (data leakage, hallucinations, bias) and implementation of mitigations.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Experience with event-driven architectures, streaming systems (Kafka, Kinesis).
Experience with mobile/web performance optimization and accessibility (WCAG).
Prior experience in regulated industries (finance, healthcare) or enterprise SaaS.
Certifications: AWS Certified Solutions Architect, Azure Solutions Architect, or equivalent.
Use of developer productivity AI tools (e.g., GitHub Copilot, Tabnine) and security/quality tools for AI-assisted development is a plus.

Responsibilities

Lead end-to-end application development: define architecture, set coding standards, design APIs, and own delivery from proof-of-concept to production.
Hands-on development: contribute code across the full stack — frontend, backend, database, CI/CD pipelines, and automated testing.
Architectural ownership: make decisions on microservices vs monoliths, data models, caching, scaling, fault tolerance, security, and deployment strategy (containers, serverless, or VMs).
AI integration: design and implement AI/ML features (e.g., LLM-based assistants, recommendation engines, RAG workflows), ensuring safe, scalable, and performant deployments.
Team leadership & mentoring: lead a cross-functional delivery team (developers, QA, UI/UX, DevOps), conduct code reviews, pair programming, and career coaching.
Project management & delivery: work with product managers and stakeholders to define scope, estimate, prioritize backlog, and ensure on-time, on-budget delivery following Agile methodologies.
Quality, security & compliance: enforce automated testing, code quality checks, secure coding practices, and compliance (GDPR, SOC2, etc.) where applicable.
Operational excellence: set up monitoring, logging, incident response, performance tuning, and runbooks for SRE/devops handoff.
Continuous improvement: evaluate new technologies, recommend process improvements, and adopt engineering best practices.
Documentation & stakeholder communication: produce architecture docs, runbooks, and regular status reports to technical and non-technical stakeholders.