- Company Name
- Global IT Solutions, Inc - Texas
- Job Title
- Technical Architect (AI / ML)
- Job Description
-
**Job title:** Technical Architect (AI / ML)
**Role Summary:** Own the design, strategy, and delivery of enterprise‑grade AI solutions focused on large language models (LLMs) and Agentic AI, partnering with engineering, data science, security, and product teams to build scalable, secure, and responsible generative AI platforms.
**Expectations:**
- 10–16 years of AI/ML experience, 6–10 years of large‑scale distributed system design, 5–8 years in cloud‑native architecture (Azure/AWS/GCP), and 5–8 years in AI platform integration.
- Proven track record in LLMs, RAG pipelines, vector databases, and generative AI applications.
- Strong communication, mentorship, and Agile/Scrum fluency.
- Ability to translate strategic vision into technical architecture and operational excellence.
**Key Responsibilities:**
1. Design and architect scalable, secure AI/ML/LLM platforms (data, model, inference pipelines).
2. Define enterprise reference architectures, reusable components, best practices, and governance for AI adoption.
3. Integrate cloud‑native, open‑source, and enterprise tooling (vector databases, feature stores, registries, orchestration frameworks).
4. Build automated MLOps/LLMOps workflows—deployment, monitoring, observability, compliance, performance tuning.
5. Collaborate cross‑functionally to align platform capabilities with business objectives and drive adoption.
6. Provide platform enablement, architecture reviews, and solution advisory to GenAI teams.
7. Conduct technology research, PoCs, benchmarking, and evaluate emerging AI tools and frameworks.
8. Drive knowledge sharing via documentation, workshops, and community initiatives.
9. Guide cost estimation, usage monitoring, FinOps, and capacity planning.
10. Partner with security, compliance, and cloud teams to ensure regulatory and data‑privacy alignment.
**Required Skills:**
- Architecting distributed microservices, serverless, event‑driven systems.
- Cloud‑native expertise (Azure, AWS, GCP) – networking, storage, compute, GPU workloads, managed AI services.
- AI platform tools: Databricks, Vertex AI, Azure AI Studio, AWS Bedrock, LangChain, PromptFlow, Ray, Kubeflow, MLflow, Airflow, Kafka.
- Vector database integration: Pinecone, Weaviate, FAISS, Milvus, Qdrant, Elastic, OpenSearch, Cosmos DB Vector.
- LLM and Agentic AI: RAG pipelines, embeddings, tokenization, prompt engineering, hallucination reduction, multi‑agent orchestration.
- Generative AI application building with LangChain, LlamaIndex, Graph RAG.
- MLOps/LLMOps: CI/CD, versioning, rollback, model observability, drift monitoring, logging, tracing, metrics, experiment tracking, governance.
- Containerization, Kubernetes, service mesh (Istio).
- Feature stores, knowledge graphs, ontology, metadata platforms.
- Evaluation frameworks: RAGAS, Promptfoo, LangSmith, TruLens.
- Agile/Scrum, Jira/Azure DevOps experience.
- Strong interpersonal, communication, analytical, problem‑solving, and feedback‑oriented skills.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
- Relevant certifications in cloud architecture (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert, GCP Professional Cloud Architect) and AI/MLOps are preferred.