NextLink Labs is a fast growing technology firm focused on helping companies build, scale, and secure their software applications and organizations. We believe that in order for companies, teams, and products to succeed, technology must be utilized effectively and securely. We pride ourselves in helping our clients win in their respective industries.
As a remote-first company with team members spread out all across the country, NextLink Labs continuously works to ensure our work environment is comfortable and collaborative. We also aim to maintain an inclusive work environment where everyone can thrive professionally and live full lives outside of work.
NextLink Labs is hiring a Forward Deployed Engineer to join our growing AI practice. You'll work directly with clients on the design, build-out, and roll-out of production AI systems, collaborating closely with an AI Architect and focusing on the robust data infrastructure that makes AI applications work in the real world, using tools like Airflow, Snowflake, BigQuery, and Databricks.
As a "forward deployed" engineer, you'll sit close to the customer's problem: discovering use cases, prototyping rapidly, hardening what works, and shipping it into the client's environment. Your time will be spent on hands-on engineering, delivering ETL/ELT pipelines for AI-powered applications, RAG pipelines, agentic workflows, and the supporting cloud infrastructure. This role has a strong consultative dimension and is distinct from pure backend development or pure research positions.
As a Forward Deployed Engineer, you'll partner directly with clients to translate ambiguous business problems into production AI systems. You'll build the data infrastructure that underpins AI applications, develop RAG and agentic workflows on top of modern LLMs, write the evaluation suites and guardrails that keep those systems safe and performant, and integrate the whole thing into clients' existing software environments.
Between and during engagements, you'll contribute to NextLink's internal accelerators and reference implementations so the broader AI practice gets stronger over time. You'll need to be technically deep, comfortable in ambiguity, and energized by direct client contact.
Data Engineering
Design and build robust ETL/ELT pipelines and data infrastructure for AI-powered applications, ensuring high quality and availability of data
Implement end-to-end data pipelines for Retrieval-Augmented Generation (RAG), including data ingestion, chunking, embedding generation, vector store management, and retrieval optimization
ML Ops and Production Systems
Write evaluation suites and guardrails to measure quality, safety, cost, and latency before and after deployment
Integrate AI components into existing client systems via REST/GraphQL APIs, event streams, and cloud-native services on AWS, Azure, or GCP
Applied AI Engineering
Build production-grade applications on top of LLMs (Claude, GPT, open-source models), including chat interfaces, copilots, agents, and back-office automation
Develop agentic workflows using frameworks such as the Claude Agent SDK, LangGraph, or comparable tools, including tool use, planning, and multi-step orchestration
Run rapid prototyping sprints with clients, getting something demo-able in days, then iterating toward production
Document architectures, share learnings with the broader NextLink AI practice, and contribute to internal accelerators and reference implementations
3–5 years of professional software engineering experience, with at least 1 year shipping AI/ML or LLM-based features to production
Strong Python skills; comfort with at least one of TypeScript/JavaScript, Go, or Java for integration work
Hands-on experience building applications with modern LLM APIs (Anthropic, OpenAI, Azure OpenAI, AWS Bedrock, etc.)
Background with data engineering tooling such as dbt, Airflow, Dagster, Snowflake, BigQuery, or Databricks
Working knowledge of RAG patterns, embedding models, and at least one vector store (pgvector, Pinecone, Weaviate, OpenSearch, etc.)
Solid grasp of one major cloud platform (AWS, Azure, or GCP), including how to deploy containerized services and manage secrets/IAM
Experience writing tests, instrumenting code, and reasoning about observability, including for non-deterministic systems
Strong written and verbal English; comfortable presenting technical work to client engineering teams and non-technical stakeholders
Customer-facing instincts: you ask good questions, manage ambiguity well, and don't disappear when a problem gets messy
Prior experience in a consulting, agency, or forward-deployed/solutions-engineering role
Experience with agent frameworks (Claude Agent SDK, AWS Strands, etc.) and MCP (Model Context Protocol)
Familiarity with prompt engineering, evals frameworks, and structured-output techniques
Infrastructure-as-Code experience (Terraform, Pulumi, CDK) and CI/CD pipelines
Exposure to regulated industries (financial services, healthcare) and the data-handling practices they require
Contributions to open-source AI tooling or technical writing/speaking on applied AI topics
We're a technical consulting firm that values clarity, ownership, and outcomes
Remote-first, with a strong written communication culture (docs, async updates, clear PRs)
Investment in your growth, including access to LLM playgrounds, an internal AI guild, and senior architects to learn from
The opportunity to build something big and exciting at the frontier of applied AI
