Senior AI Tech Lead

Job Title: Senior AI Tech Lead

Location: Jersey City, NJ
Employment Type: Full Time
Job Type: Permanent / Direct Hire

Job Summary

We are seeking a hands-on Senior AI Tech Lead to design, develop, and lead enterprise-grade Generative AI, Agentic AI, and cloud-native AI solutions.

The ideal candidate will have strong expertise in Python, TypeScript/JavaScript, AWS AI services, LLMs, RAG, AI agents, vector databases, API development, microservices, event-driven architecture, and containerized deployments.

This role requires a technical leader who can architect AI solutions while remaining hands-on with development, integrations, orchestration, deployment, and production optimization.

Required Qualifications

• Strong hands-on experience with Python and TypeScript / JavaScript.

• Strong experience developing REST APIs, microservices, and cloud-native applications.

• Experience with event-driven architecture and asynchronous processing.

• Experience with containerized application deployments, including Docker and Kubernetes.

• Strong hands-on experience with AWS cloud and AI/ML services.

• Experience with Amazon Bedrock, Amazon SageMaker, AWS Lambda, Step Functions, ECS/EKS, API Gateway, S3, and CloudWatch.

• Strong understanding of Generative AI, LLMs, Prompt Engineering, RAG, AI Agents, and Agent Orchestration.

• Experience integrating LLMs, vector stores, embeddings, tools, APIs, and external services.

• Experience implementing AI guardrails, model evaluation, security, and responsible AI practices.

• Experience designing and implementing CI/CD pipelines for AI and cloud-native applications.

Key Responsibilities

<>AI & GenAI Architecture

• Design and implement scalable Generative AI and Agentic AI solutions for enterprise use cases.

• Architect AI applications using LLMs, RAG, vector databases, embeddings, APIs, and AI agents.

• Design AI agent capabilities including agent orchestration, tool calling, tool integration, memory, context management, and workflow automation.

• Develop reusable AI services, API layers, RAG services, and tool connectors.

• Evaluate AI models and technologies for performance, quality, cost, security, and scalability.

• Implement AI guardrails, model evaluation, responsible AI, and security controls.

<>AWS Cloud & AI

• Design and develop AI workloads using AWS Bedrock and SageMaker.

• Build serverless and event-driven AI applications using AWS Lambda and Step Functions.

• Develop containerized AI services using Amazon ECS / EKS.

• Design API layers using Amazon API Gateway.

• Leverage Amazon S3 for data and AI application workflows.

• Implement application monitoring, logging, and observability using Amazon CloudWatch.

• Integrate AWS AI/ML services with enterprise applications and data platforms.

• Optimize cloud architectures for scalability, reliability, security, and cost.

<>Software Engineering & Integration

• Develop production-quality applications using Python and TypeScript/JavaScript.

• Design and implement RESTful APIs and microservices.

• Develop event-driven solutions using asynchronous messaging and integration patterns.

• Build integrations between AI agents, enterprise applications, APIs, databases, and external tools.

• Design scalable API gateways, service layers, tool connectors, and AI integration frameworks.

• Implement automated testing, deployment, monitoring, and production support.

<>RAG & Vector Search

• Design and implement Retrieval-Augmented Generation (RAG) architectures.

• Develop document ingestion, chunking, embedding, retrieval, ranking, and context-generation pipelines.

• Integrate vector databases / vector stores with LLM applications.

• Implement semantic search and similarity search using embeddings.

• Optimize retrieval quality, relevance, latency, and cost.

• Integrate RAG services with enterprise APIs and data sources.

Must-Have Technical Skills

Programming & Development

• Python

• TypeScript

• JavaScript

• REST APIs

• Microservices

• API Development

• Event-Driven Architecture

• Asynchronous Processing

• Docker

• Kubernetes

AWS

• AWS Bedrock / Amazon Bedrock

• Amazon SageMaker

• AWS Lambda

• AWS Step Functions

• Amazon ECS

• Amazon EKS

• Amazon API Gateway

• Amazon S3

• Amazon CloudWatch

• AWS IAM

• AWS Cloud Architecture

Generative AI / Agentic AI

• Generative AI / GenAI

• Large Language Models / LLMs

• AI Agents

• Agentic AI

• Prompt Engineering

• Agent Orchestration

• Tool Calling / Function Calling

• Tool Integration

• AI Workflows

• AI Guardrails

• Model Evaluation

• LLM Evaluation

• AI Security

RAG & Data

• Retrieval-Augmented Generation / RAG

• Embeddings

• Vector Databases

• Vector Stores

• Semantic Search

• Context Management

• Knowledge Retrieval

• RAG Pipelines

AI Architecture & Engineering

• LLM application architecture

• AI API integration

• AI service development

• Agent orchestration

• Multi-step AI workflows

• Tool connectors

• Enterprise AI integration

• Model evaluation and optimization

• Prompt engineering

• AI observability

• AI security and guardrails

• Production AI deployment

• Cloud-native AI architecture

DevOps & Cloud-Native Skills

• CI/CD

• DevOps

• Docker

• Kubernetes

• Amazon ECS

• Amazon EKS

• Infrastructure as Code

• Automated Build & Deployment

• Cloud Monitoring

• Logging & Observability

• Application Performance Monitoring

• Cloud Security

• Scalable Microservices

Preferred Qualifications

• Experience building enterprise Generative AI / Agentic AI applications from prototype through production.

• Experience with multiple LLM providers such as OpenAI, Anthropic, or Google Gemini.

• Experience with AI agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel.

• Experience with vector databases such as Pinecone, FAISS, Weaviate, OpenSearch, or Milvus.

• Experience with RAG, Graph RAG, hybrid search, embeddings, and knowledge retrieval.

• Experience with MCP (Model Context Protocol), tool calling, function calling, and agent-to-agent workflows.

• Experience implementing LLM evaluation, AI safety, governance, and guardrails.

• Experience with enterprise API management and integration platforms.

• AWS certification or relevant AI/Cloud certification is a plus.

Leadership Responsibilities

• Provide technical leadership for AI engineering and architecture initiatives.

• Define reusable AI architecture patterns, development standards, and engineering best practices.

• Mentor engineers and provide technical guidance on AI application development.

• Conduct architecture and code reviews.

• Collaborate with cloud, data, security, application, and product teams.

• Translate business requirements into scalable AI and cloud architectures.

• Drive technical decisions around AI frameworks, models, APIs, infrastructure, and deployment strategies.

Core Dice Search Keywords

Senior AI Tech Lead, AI Tech Lead, AI Technical Lead, Generative AI Lead, GenAI Tech Lead, AI Architect, GenAI Architect, AI Engineer, Senior AI Engineer, AI Solutions Architect, Machine Learning Engineer, LLM Engineer, Generative AI Engineer, Agentic AI, AI Agents, Generative AI, GenAI, LLM, Large Language Models, RAG, Retrieval Augmented Generation, Prompt Engineering, Agent Orchestration, AI Agent, Tool Calling, Function Calling, Vector Database, Vector Store, Embeddings, Semantic Search, Python, TypeScript, JavaScript, REST API, Microservices, Event Driven Architecture, Docker, Kubernetes, AWS, Amazon Web Services, AWS Bedrock, Amazon Bedrock, SageMaker, AWS Lambda, Step Functions, ECS, EKS, API Gateway, S3, CloudWatch, CI/CD, DevOps, Cloud Native, LangChain, LangGraph, AutoGen, CrewAI, OpenAI, Anthropic, Claude, Gemini, Pinecone, FAISS, Weaviate, OpenSearch, Milvus, MCP, Model Context Protocol, AI Guardrails, LLM Evaluation, AI Security, AI Governance.

Education

Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Information Technology, or a related technical field preferred.

Location

Jersey City, NJ

Employment Type

Full Time / Permanent