AWS AI SME / Architect

AWS AI SME / Architect

Location: Jersey City, NJ (Onsite Day 1, Hybrid – 3 Days in Office)

Overview

We are seeking an exceptional hands-on Technical Lead to spearhead an enterprise Generative AI (GenAI) engineering program. This role is ideal for a seasoned technologist with deep AI/ML expertise and strong engineering skills, capable of both architecting and implementing enterprise-grade GenAI solutions.

You will lead initiatives around AI agents, advanced RAG architectures, and platform engineering — while remaining directly involved in technical execution.

Key Responsibilities

1. Technical Leadership & Development

• Lead the design and deployment of enterprise-scale GenAI solutions.
• Use hybrid custom-developed and open-source platforms (e.g., Dify, OpenWebUI).
• Architect AI agents using Python, LlamaIndex, and LangGraph.
• Provide hands-on coding and technical mentorship to the team.
• Establish best practices for GenAI solution development, deployment, and operations.

2. AI/ML Engineering

• Design and implement LLM-based solutions with model architecture and fine-tuning.
• Apply classical ML where applicable to enhance GenAI use cases.
• Implement RAG patterns and vector databases.
• Optimize AI pipelines for scalability, performance, and cost-efficiency.

3. Context Engineering & Advanced RAG

• Design advanced context strategies for LLM optimization.
• Build advanced RAG systems with multi-hop reasoning, hybrid search, and re-ranking.
• Implement context window optimization, dynamic context selection, and self-improving feedback loops.

4. LLM Optimization & Fine-Tuning

• Fine-tune domain-specific LLMs using LoRA, QLoRA, and other techniques.
• Optimize model performance via quantization, pruning, and distillation.
• Build benchmarks for performance tracking.
• Improve inference latency, throughput, and prompt optimization.

5. Platform & Infrastructure

• Design event-driven architectures for scalable, asynchronous AI processing.
• Deploy containerized AI applications via Kubernetes on AWS.
• Utilize AWS services: SageMaker, Bedrock, Lambda, EKS, SQS, SNS.
• Establish CI/CD pipelines for model and application delivery.

6. Security & Governance

• Apply secure design principles and AI security frameworks.
• Ensure compliance with data privacy, access controls, and ethical AI standards.
• Build monitoring and audit trails for AI system behavior.

Required Qualifications

AI/ML Expertise

• Deep understanding of LLM architecture, training, and deployment.
• Advanced prompt and context engineering skills.
• Experience with RAG systems (hybrid search, agentic RAG).
• Proven LLM fine-tuning for domain-specific applications.
• Strong background in classical ML algorithms.
• Expertise in Python and ML libraries (PyTorch, TensorFlow, Pandas, NumPy).
• Familiarity with GenAI frameworks (LangChain, LlamaIndex, LangGraph).

Engineering Excellence

• Proven AWS cloud architecture experience.
• Expertise in event-driven and scalable architectures.
• Strong containerization skills (Docker, Kubernetes).
• MLOps/DevOps: CI/CD, infrastructure as code, ML lifecycle management.

Security & Enterprise Standards

• Secure coding and architectural best practices.
• AI-specific security (prompt injection prevention, adversarial robustness).
• Familiarity with enterprise authentication and compliance.

Leadership & Communication

• 12+ years in technical roles, with 5+ in AI/ML.
• Experience leading technical teams while staying hands-on.
• Strong communication and stakeholder management skills.

Preferred Qualifications

• Multi-agent systems and agent orchestration.
• Familiarity with vector databases (Qdrant, OpenSearch, pgvector).
• Deep understanding of embedding models and semantic search.
• Open-source AI/ML contributions.
• Model quantization, edge deployment, and graph-based RAG experience.
• Certifications in AWS, Kubernetes, or AI/ML platforms.