AI Lead Engineering Enterprise AI, Cloud & Platform Transformation

Overview

Four direct-hire AI engineering roles based in New York, Bay Area, Dallas, or Chicago. Hybrid work model (no fully remote option); approximately 25% travel when necessary. Exact weekly onsite schedule pending. Compensation guidance: USD 150,000–200,000 annual base, with potential flexibility up to USD 220,000 for exceptional candidates subject to approval. Benefits include medical/dental/vision coverage, disability and life insurance, 401(k) with company match, paid leave, and parental leave. A performance-based bonus is indicated with individual terms to be confirmed. US citizens and Green Card holders preferred; H1B transfers possible. OPT, STEM OPT, and EAD candidates may be considered where they have completed a master's degree and started working in the US, subject to individual confirmation. For H4-EAD or other authorization, provide category, expiration date, and sponsorship/transfer requirements. No minimum two-year EAD validity requirement confirmed. Candidates must join the hiring employer directly (not BestInfos W-2 payroll).

Forward Deployed Engineer — Agentic AI

Combines senior client engagement with hands-on engineering, translating business challenges into working AI solutions and taking them from rapid proofs of value toward MVP and production delivery.

Required Background

• 8+ years in full-stack engineering, solution architecture, or technical consulting with significant client-facing delivery
• Strong Python plus TypeScript, Java, or Go
• Practical experience with LLMs, agent orchestration, tool calling, planning/execution loops, and memory patterns
• RAG, embeddings, chunking, vector databases, and retrieval evaluation
• APIs, data pipelines, enterprise integrations, and sound software architecture
• Cloud-native engineering on AWS, Azure, or GCP including containers, CI/CD, and observability
• Ability to explain technical decisions and measurable business value to senior stakeholders

Preferred

• Production GenAI experience
• Integrations with platforms such as ServiceNow, Salesforce, SAP, Microsoft, Databricks, or Snowflake
• Engineers who personally build solutions, develop evaluation methods, and address security, access controls, human oversight, reliability, latency, and cost

AI Prompt Engineer / Agentic AI Engineer

Hands-on engineering role focused on developing agents that reason, use tools, and execute enterprise workflows.

Key Requirements

• Experience building LLM applications and agent workflows using Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or comparable frameworks
• Hands-on MCP servers, tools, API orchestration, and enterprise connectivity
• Python, TypeScript, or similar programming languages
• Prompt optimization, agent planning, memory, context management, and multi-step execution
• RAG, embeddings, vector databases, and knowledge grounding
• Familiarity with Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, or comparable platforms
• Agent evaluation, testing, deployment, monitoring, troubleshooting, security, and governance

Preferred

• Enterprise copilots, AI assistants, and healthcare or regulated-system integrations

No minimum years of experience specified; assess demonstrated engineering depth rather than imposing the FDE or platform-engineering experience threshold.

AI Lead Engineering — Enterprise AI, Cloud & Platform Transformation

Combines AI engineering with enterprise platform design, cloud transformation, and production operations.

Required Experience

• 12+ years across AI, DevOps, cloud engineering, SRE, or platform engineering
• 3+ years hands-on AI-stack experience
• 5+ years designing enterprise platforms
• 3+ years implementing AIOps or observability platforms (must be demonstrated)
• Enterprise cloud transformation, production AI/ML environments, and mission-critical or regulated platforms
• Strong coordination with architecture, operations, security, and delivery teams
• Python mandatory

Relevant Technologies

• GenAI, LLMs, RAG, AgentCore, MCP, AWS Bedrock, SageMaker, vector databases, model lifecycle management, AWS/Azure/GCP
• Engineering copilots such as Kiro or GitHub Copilot
• Infrastructure automation, CI/CD, Kubernetes, Docker, service meshes
• Tools such as Terraform, Datadog, Dynatrace, Splunk, Prometheus, OpenTelemetry (listed as preferred; not every named product required)

AI Architect / FDE — Healthcare

Focuses on AI-enabled enterprise architecture, GCP engineering, and integration in a healthcare environment.

Required Background

• 12+ years overall IT experience with substantial cloud, enterprise, integration, or AI/ML architecture experience
• Strong hands-on GCP and cloud-native architecture
• Vertex AI, GenAI solution design, prompt engineering, model integration, governance, and production deployment considerations
• Strong Java and Spring Boot including microservices, REST APIs, security, and scalable backend design
• Enterprise integration connecting modern and legacy platforms
• Ability to evaluate and apply AI productivity tools such as Claude and Antigravity
• Strong consulting and communication skills with senior business and technology stakeholders

Preferred

• Healthcare, clinical operations, payer/provider integration, regulated environments
• Relevant Google Cloud certifications

GCP/Vertex AI and Java/Spring Boot are central requirements for this search.

Application Requirements

For each qualified and interested candidate, provide:

• Updated resume/profile in Word format
• Completed summary table (role, contact details, current employment status/title/company, work authorization and sponsorship needs, travel acceptance, role-specific skills to establish fit)
• Brief role-fit note identifying the proposed role and strongest evidence of relevant engineering experience
• Confirmed interest and interview availability including time zone
• Two or three sentences covering a technical example (solution, technologies, production status, results) only if the existing profile does not clearly demonstrate the candidate's personal contribution
• For the AI Lead Engineering role, capture required AI, platform-design, and AIOps/observability experience separately

Assessment & Interview Process

• 45-minute CoderPad assessment covering cloud expertise
• Technical panel and client interviews
• HR discussions
• Sequence follows instructions for each candidate; CoderPad does not necessarily precede L1 for candidates already in process
• Candidates can familiarize themselves with the platform at https://coderpad.io/ (general website, not assessment link)
• At assessment stage, coordinate availability beforehand and explain the test must be completed within 24 hours of receiving the individual link