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AI Engineer
INVOKE
Montreal, CAN
Full Time
Mid
1 weeks ago
PythonJavaNode.jsC#LLMRAG
Free
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PythonJavaNode.js
About the Role
INVOKE is seeking an AI Engineer to build LLM-powered automations, chat/voice assistants, and intelligent agents. You will design RAG pipelines, implement tool/function calling, and deploy solutions using FastAPI or Node.
Key Skills for This Role
PythonJavaNode.jsC#LLMRAG
Responsibilities
- Build LLM powered automations, chat/voice assistants, and intelligent agents that integrate with client systems
- Design and deploy retrieval augmented generation (RAG) pipelines including document ingestion, chunking, embeddings, vector search, and grounding
- Implement tool/function calling and multi step agent workflows with human in the loop processes
- Package solutions as reliable services (FastAPI or Node.js) with tests, observability, and CI/CD pipelines; deploy to 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
- Implement guardrails and safety mechanisms including content filters, PII redaction, schema/JSON validation, fallbacks, and model routing
- Collaborate with product, delivery, and client stakeholders to scope use cases, run quick proofs of concept (POCs), and scale prototypes to production
- Participate in code reviews and contribute to improving internal templates, tooling, and documentation
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 2 3 years of software engineering experience using Python, Java, Node.js, C#, or similar
- 1 2 years hands on experience building with LLMs (prompt engineering, RAG, agents, tool/function calling, structured outputs, 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 ingestion/embedding pipelines
- Familiarity with evaluation and observability tools (e.g., LangSmith, RAGAS/DeepEval, OpenTelemetry) and automated tests for LLM workflows
- Basic DevOps/MLOps skills: Docker, Kubernetes or serverless, CI/CD (GitHub Actions), secrets/IAM
- Exposure to cloud platforms (AWS, Azure, GCP) and related services
- Strong problem solving and communication skills
Full Job Posting
Company Overview
- INVOKE provides technology, implementation, and lifecycle expertise for intelligent automation like RPA and AI.
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.js) for internal tools.
- Strong security mindset, including familiarity with OAuth/JWT, least privilege access, PII handling, and compliance best practices.
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