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.

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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