About the role

We are looking for a Senior AI Engineer to design, build andproductionizeenterprise-grade AI applications, with a strong focus on LLM-based solutions (commercial models), RAG pipelines, document automation, and integration with existing products and platforms. This role will complement a team with strong DevOps,fullstackand backend capabilities by bringing deepexpertiseinLLMOps, evaluation, AI architecture, and hands-on delivery of production AI systemsoperatingunder real-world traffic and reliability constraints.

Whatyou’lldo (responsibilities)

Design and implement AI-powered features and applications (assistants, enterprise search, Q&A over documents, extraction/classification, workflow automation).

Build robust integrations with commercial LLM APIs (e.g., Azure OpenAI/OpenAI) using Python, including streaming, function calling/tool use, errorhandlingand cost controls.

Architect and develop RAG end-to-end:embeddings and indexing (vector/hybrid search),retrieval, reranking, citations/grounding, “no-answer” patterns.

EstablishLLMOpspractices:prompt/version management,automated evaluations and regression tests,tracing/observability,monitoringand alerting,deploymentpatternsand runbooks.

Define and implement security & governance controls for enterprise environments:

RBAC/ABAC and document-level permissions,PII handling (redaction/masking),

mitigation of prompt injection and data exfiltration,auditabilityand compliance-ready logging.

Provide technical leadership to full-stack developers by defining end-to-end AI architecture,setimplementation patterns, and guiding delivery of production-ready services (CI/CD, secrets, scaling, reliability).Produce high-quality technical documentation and contribute reusable components/accelerators for faster delivery.

Whatyou’llbring (minimum qualifications)

Strong software engineering skills with Python (API development, clean code, testing).

Experience integrating external APIs and building backend services (REST, auth, retries, rate limiting).

Hands-on experience building LLM applications using commercial model APIs.

Proven ability to provide technical leadership to full-stack developers, including designing end-to-end AI architecture and guiding implementation decisions.Hands-on experience deploying AI models and services to production environments, including operating under real-world traffic, performance, reliability, and monitoring requirements.

Hands-on experience with Document AI, data ingestion pipelines, and unstructured data processing for document-centric automation use cases.

Preferred qualifications

Experience with Azure services (Azure OpenAI, Azure AI Search, Storage, Key Vault, Functions/App Service/Container Apps).

Familiarity with frameworks such asLangChainLlamaIndex Semantic Kernel (or equivalent).

Experience with hybrid search, reranking, evaluation frameworks, and observability (OpenTelemetryor similar).

Experience with advanced AIarchitecturesuch as Multi-Agent Systems (MAS) oradvancedapproaches, including graph-based RAG.

Priorworkin regulated/enterprise environments and/or consulting delivery models.

Key skills (summary)

Python,FastAPIFlask, testing (pytest), Git

LLM APIs, prompt engineering, function calling/tool use

RAG: chunking, embeddings, vector DB/search, hybrid retrieval, reranking, citations, grounding, evaluation

LLMOps prompt/versioning, evaluation, monitoring, tracing

Document AI, OCR, ingestion pipelines, unstructured data processingSecurity: PII, access control, prompt injection mitigations

Architecture & leadership: end-to-end AI system design, technical guidance for full-stack teams

Cloud & DevOps: Docker, CI/CD, logging/metrics, scalable deployment