AI Technical Lead

AI Solutions ArchitectKey ResponsibilitiesArchitect, build, and own end-to-end Agentic AI solutions on AWS — from design through production deployment.Lead and mentor a cross-functional Scrum team of engineers, ML practitioners, and data specialists.Define technical standards, code-review practices, and engineering best practices for the team.Design and implement RAG pipelines, LLM-powered agents, and multi-agent orchestration frameworks.Drive cloud architecture decisions: serverless, microservices, containers (ECS/EKS), and data services on AWS.Collaborate with Product Owners to translate business requirements into robust technical solutions.Actively participate in sprint ceremonies — planning, stand-ups, retrospectives — as the technical authority.Establish CI/CD pipelines, infrastructure-as-code (IaC), and automated testing strategies.Evaluate and integrate emerging AI tools, models, and frameworks into the product roadmap.Ensure security, scalability, observability, and cost-efficiency of all cloud-based AI workloads.Required ExpertiseAWS Full-Stack EngineeringHands-on experience with core AWS compute, storage, networking, and AI/ML services.Lambda, ECS/EKS, EC2, API Gateway, S3, RDS, DynamoDB, Bedrock, SageMaker, Step FunctionsIAM, VPC, CloudWatch, CloudFormation / CDK / TerraformProven ability to design resilient, highly available, and cost-optimised architectures (Well-Architected Framework).CI/CD implementation using CodePipeline, GitHub Actions, or equivalent tooling.Artificial Intelligence, RAG & AgentsPractical experience with LLM integration (OpenAI, Anthropic Claude, AWS Bedrock, Mistral, or similar).Design and deployment of Retrieval-Augmented Generation (RAG) pipelines at scale.Embedding models, vector stores (Pinecone, pgvector, OpenSearch, Weaviate), chunking strategiesBuilding autonomous and multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Amazon Bedrock Agents.Prompt engineering, chain-of-thought reasoning, tool/function calling, and agent memory management.Evaluation, monitoring, and observability of AI systems (hallucination detection, latency, cost tracking).Python DevelopmentExpert-level Python for backend services, data processing, and AI/ML workflows.Proficiency with key libraries: FastAPI / Flask, Pydantic, asyncio, boto3, LangChain / LlamaIndex.Strong software engineering fundamentals: clean code, SOLID principles, unit and integration testing.Experience supporting project-to-operations transition in onsite, client-facing environmentsBased in Houston, TX with the ability to work out of the Woodside client siteMust Have CapabilitiesAWS ArchitecturePython ExpertLLM IntegrationRAG PipelinesAgent FrameworksCI/CD & IaCScrum / Agile LeadershipAPI DesignCloud SecurityProduction AI SystemsDemonstrable hands-on delivery (not just oversight) of AI-powered, cloud-native applications.Strong execution discipline with experience in tracking deliverables, managing competing priorities, and ensuring quality outcomes.Strong communication skills to bridge technical depth with business stakeholders.Track record of delivering production systems within Agile sprints.Nice to HaveExperience with AWS Bedrock Agents, Knowledge Bases, or Guardrails.Knowledge of fine-tuning or RLHF for domain-specific LLM adaptation.Familiarity with graph databases (Neptune) or knowledge graphs for agent reasoning.Frontend experience (React, Next.js) for full-stack ownership of AI-powered interfaces.Data engineering background: Glue, Athena, Redshift, or Spark.Exposure to MLOps practices and tooling (MLflow, W&B, SageMaker Pipelines).AWS certifications: Solutions Architect Professional, Machine Learning Specialty.Contributions to open-source AI / ML projects.Experience with multi-modal AI (vision, speech, embeddings beyond text).Ideal ProfileThe successful candidate is a builder at heart — someone who moves fluidly between whiteboard architecture and writing production code. They are naturally curious about the AI landscape, stay ahead of rapidly evolving agent frameworks, and bring a pragmatic engineering mindset that turns experimentation into reliable, scalable products.10+ years of software engineering experience, with at least 3–5 years in cloud-native AWS environments.2+ years of hands-on work with LLMs, RAG, or autonomous AI agents in a production context.Prior experience leading technical delivery within a Scrum or scaled-Agile (SAFe) team.A portfolio or demonstrable examples of AI-powered products shipped to production.