AI Engineer

Invoke is a consulting and Innovation firm focused on delivering Intelligent automation solutions that solve real operational challenges. We partner with organizations across industries, including the public sector, to design and implement scalable RPA and AI-driven workflows that improve efficiency, accuracy, and reliability.

Learn more about the general tasks related to this opportunity below, as well as required skills.

Our teams work hands-on with clients to automate complex processes across a range of systems, from modern applications to legacy environments. A significant portion of our work is within the public sector, where we help government organizations streamline high-volume processes, improve data quality, and modernize critical operations through automation.

We are a fast growing organization, always looking for new members to our family. INVOKE provides the technology, implementation, and lifecycle expertise to solve business challenges through the lens of intelligent automation technologies like Robotic Process Automation and Artificial Intelligence.

As this role supports public sector clients, U.S. citizenship is required.

What You’ll Be Doing Build

LLM-powered automations , chat/voice assistants, and intelligent agents that integrate seamlessly with client systems. Design and deploy

retrieval-augmented generation (RAG)

pipelines, including document ingestion, chunking, embeddings, vector search, and grounding for accurate, auditable responses. Implement

tool/function calling

and multi-step agent workflows to perform actions (draft → review → execute → verify), incorporating

human-in-the-loop

processes where necessary. Package solutions as reliable services (e.g.,

FastAPI

or

Node.js ) with tests, observability, and CI/CD pipelines; deploy to the cloud using

serverless

or

containerized

architectures. Instrument, evaluate, and tune LLM solutions—manage tracing, latency and cost budgets, prompt/version control, A/B tests, and golden-set evaluations to reduce hallucinations and improve output quality. Implement

guardrails and safety mechanisms , including content filters, PII redaction, schema/JSON validation, fallbacks, and model routing across providers. Collaborate with product, delivery, and client stakeholders to scope use cases, run quick

proofs of concept (POCs) , and scale successful prototypes into production-ready systems. Participate in

code reviews

and contribute to improving internal templates, tooling, and documentation to enhance reliability and development speed. Occasionally support hiring initiatives through interviews or technical assessments.

Qualifications Bachelor’s or Master’s

degree in Computer Science, Engineering, or a related field. 2–3 years

of experience in software engineering using one or more programming languages:

Python, Java, Node.js, C#,

or similar. 1–2 years

of hands-on experience building with

LLMs —prompt engineering, RAG, agents, tool/function calling, structured outputs, and streaming. Proficiency with

LLM SDKs and frameworks

(e.g., OpenAI/Azure OpenAI, Anthropic, Google, LangChain, LlamaIndex) and

vector databases

(e.g., Pinecone, Weaviate, pgvector/Postgres, FAISS). Experience preparing

unstructured data

(PDFs, HTML, emails, tickets) and developing robust ingestion/embedding pipelines and document stores (e.g., S3, GCS). Familiarity with

evaluation and observability tools

(e.g., LangSmith, RAGAS/DeepEval, OpenTelemetry, logging) and experience writing

automated tests

for LLM workflows. Basic

DevOps/MLOps

skills: Docker, Kubernetes or serverless (Lambda, Cloud Run), CI/CD (GitHub Actions), and secrets/IAM best practices. Exposure to

cloud platforms

(AWS, Azure, GCP) and related services (API Gateways, managed databases/queues; Bedrock or Azure OpenAI experience is a plus). Strong

problem-solving and communication skills ; ability to translate business workflows into practical automations and clearly explain trade-offs to non-technical stakeholders.

Nice to Have Experience with

fine-tuning

or

LoRA adapters

for domain-specific tasks; knowledge of

prompt caching

and

cost optimization

strategies. Experience integrating with

enterprise applications

and knowledge bases, and developing lightweight

admin UIs

(React, Next. xsgimln js) for internal tools. Strong

security mindset , including familiarity with OAuth/JWT, least-privilege access, PII handling, and compliance best practices