Cloud AI Engineer

Spectraforce; Newark (NJ); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Spectraforce is seeking a GenAI Cloud Engineer in Newark, NJ, hybrid (3 days onsite) for a 12-month contract with potential conversion. The role focuses on building enterprise-scale agentic AI using AWS Bedrock, AgentCore, data products, and governed cloud-native architectures.

You will implement runtimes, memory, tooling, observability, and guardrails while collaborating with architects, data teams, and Responsible AI stakeholders. 5–8 years preferred for senior leadership tasks.

Qualifications

• Hands-on delivery of cloud-native applications or AI/ML-enabled platforms.
• Experience with microservices, APIs, event-driven workflows, and secure enterprise integrations.
• Proficiency with AWS services, including serverless, containers, IAM, observability, and deployment patterns.

Responsibilities

• Build and deploy enterprise AI agents using Bedrock AgentCore and related services.
• Implement data ingestion, semantic retrieval, embeddings, vector search, and memory pipelines.
• Develop guardrails, workflow orchestration, and escalation patterns for agentic apps.

Skills

Cloud-native apps

AWS Bedrock

AgentCore/Bedrock

Multi-agent orchestration

Security & IAM

Observability

Memory & embeddings

Tools

LangGraph

LangChain

Strands

MCP

A2A protocols

Bedrock AgentCore

Job description

Location: Newark, NJ (Hybrid – 3 days onsite)

Duration: 12 months (possibility of conversion)

About The Role

We are seeking a GenAI Cloud Engineer to build and deploy enterprise-scale agentic AI solutions that support complex business workflows, decision support, operational intelligence, customer engagement, and other signal-to-action use cases across the enterprise.

This is a hands-on engineering role responsible for designing, developing, and delivering production-grade AI agents using AWS Bedrock, Amazon Bedrock AgentCore, enterprise data products, semantic layers, and governed cloud-native architectures. You will implement agent runtimes, orchestration patterns, memory, tool integration, observability, evaluation, and guardrail capabilities while partnering with architects, data teams, product owners, and Responsible AI stakeholders. The ideal candidate has recent experience building enterprise agents, agentic RAG, multi-agent workflows, and secure integrations with business systems in regulated environments.

Key Responsibilities

• Build and deploy enterprise AI agents using Amazon Bedrock AgentCore, Bedrock models, AgentCore runtime services, AgentCore Gateway, agent registry patterns, and agent-to-agent communication where appropriate.
• Implement data ingestion, semantic retrieval, embeddings, vector search, knowledge graph, and memory pipelines that allow agents to consume certified data products, Denodo/USBIE sources, Bedrock Knowledge Bases, and governed enterprise content.
• Develop intent classification, tool routing, workflow orchestration, guardrails, policy checks, human-in-the-loop controls, and fail-safe escalation patterns for agentic applications.
• Integrate agents with enterprise systems and workflows such as Salesforce/Elevate, Service Platform, CaseView, Genesys, MAC/MPI, USBIE, Denodo, APIs, MCP tools, and event streams.
• Build evaluation, monitoring, tracing, and operational controls for agents, including quality metrics, hallucination and safety checks, latency, cost, feedback loops, and incident response readiness.
• Uphold enterprise standards for application security, IAM/agent identity, data protection, auditability, Responsible AI, ADS/data-product governance, observability, and production reliability.
• Stay current with AWS AgentCore, Bedrock, agent frameworks, model evaluation approaches, memory architectures, semantic data products, and enterprise agentic AI patterns.

Leadership, Mentoring & Influence (Only For Senior 5-8 Years Of Experience)

• Lead a workstream: Take technical ownership of a defined feature area or service - from design through production support - coordinating a small group of engineers where needed.
• Mentor engineers: Guide associate and mid-level engineers through code review, pairing, and design feedback; help them build GenAI and cloud skills.
• Contribute to architecture: Participate actively in design reviews, propose solution options with clear trade-offs, and help shape the patterns and standards the team adopts.
• Influence through delivery: Partner directly with product owners, architects, and business stakeholders to refine requirements and build credibility through working software.
• Improve the team: Identify and drive improvements in engineering practices, tooling, reusable components, and documentation; share knowledge through demos and internal write-ups.

Required Qualifications

• 3-5 years of software engineering experience, with hands-on delivery of cloud-native applications or AI/ML-enabled platforms.
• Experience developing microservices, APIs, event-driven workflows, distributed applications, and secure integrations with enterprise systems.
• Hands-on experience with AWS cloud services, including serverless, container, messaging/eventing, IAM, observability, and cloud-native deployment patterns.
• Recent experience building enterprise GenAI or agentic AI solutions, including agents, agentic RAG, multi-step orchestration, chat experiences, or signal-to-action workflows.
• Experience with Amazon Bedrock, Amazon Bedrock AgentCore, or comparable enterprise LLM and agent runtime platforms.
• Experience with LangGraph, LangChain, Strands, MCP, A2A protocols, tool-use frameworks, or comparable multi-agent orchestration patterns.
• Experience with model evaluation, prompt engineering, agent quality testing, feedback loops, LLM-as-judge patterns, and reliability measurement.
• Experience with data ingestion, vector databases, embeddings, semantic search, knowledge bases, memory stores, and retrieval patterns for structured and unstructured data.
• Experience implementing AI guardrails, safety controls, intent classification, routing logic, access controls, audit logging, evaluation, and human escalation paths.
• Strong understanding of software engineering best practices, application security, Responsible AI expectations, data governance, scalability, resilience, and production operations.
• Experience mentoring engineers, leading technical delivery of a defined workstream, and collaborating with architects, product owners, data teams, and platform teams.

Preferred Qualifications

• Experience with event-driven and serverless architectures using AWS EventBridge, MSK, SNS/SQS, Step Functions, Lambda, API Gateway, and container services.
• Experience delivering AI or data products in financial services, insurance, or other regulated environments with privacy, compliance, audit, and control requirements.
• Familiarity with enterprise data products, semantic layers, Denodo, USBIE, ADS certification, knowledge graphs, Neptune/OpenSearch, and governed data consumption patterns.
• Experience integrating agents with CRM, service, telephony, document, workflow, or engagement platforms such as Salesforce, Genesys, Service Platform, MAC/MPI, or similar systems.

IDEAL CANDIDATE

You are a strong software engineer with recent enterprise GenAI and agentic AI experience who enjoys turning ambiguous business use cases into reliable, governed, production-ready agents. You understand that CNS agents must combine data products, signals, deterministic rules, LLM reasoning, memory, tool use, observability, and human controls. You can work across cloud architecture, data integration, AI orchestration, and production operations while raising the technical bar for the team.

LEVELING: SENIOR VS. DEV

Senior GenAI Cloud Engineer - 5–8 years. Owns architecture and technical direction for enterprise agentic AI capabilities, sets standards for AgentCore, data, governance, observability, and multi-agent patterns, leads complex multi-team initiatives, mentors senior engineers, and influences cross-team design decisions.

GenAI Cloud Engineer - 3+ years. Hands-on technical contributor. Leads implementation of specific agent workstreams, contributes to AgentCore architecture and delivery patterns, mentors associate and mid-level engineers, and influences within the team through working software.