Note: The job is a remote job and is open to candidates in USA. INVOKE is a fast growing organization that provides technology and lifecycle expertise to solve business challenges through intelligent automation technologies. The AI Engineer role involves building LLM-powered automations and intelligent agents, designing retrieval-augmented generation pipelines, and collaborating with stakeholders to scale successful prototypes into production-ready systems.
Responsibilities
- 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
Skills
- 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
- 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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