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Discovery call
Share your AI stack, integrations needed, and performance targets. Fit confirmed in 24 hours.
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Available now · Onboard in 7 days
Hire an AI Integration Engineer to wire LLMs, agents, and enterprise systems into one reliable pipeline.
Onboard a senior AI Integration Engineer to design APIs, RAG pipelines, agent orchestration, and MLOps that turn your AI prototypes into resilient, observable production systems.
IESKRN+9
4.9/5
Trusted by 120+ teams shipping AI products
- 5+ years building production AI systems
- LLM, RAG & Agent orchestration
- Cloud-native MLOps
- Onboard in 7 days
Why hire an AI Integration Engineer
Outcomes you can ship in the first 90 days
Reliable AI deployments
Ship AI features that stay up under load with retries, fallbacks, rate-limiting, and circuit breakers built in.
Connected enterprise systems
Bridge LLMs and agents to your CRM, ERP, data warehouse, and internal APIs without brittle glue code.
Lower latency & cost
Optimize token usage, caching, model routing, and async orchestration to cut AI infra spend by 30–60%.
Full AI observability
Trace prompts, tools, and agent steps end-to-end with metrics, logs, and alerts your SRE team trusts.
Core responsibilities
What your AI Integration Engineer will own
APIs, pipelines, agent wiring, deployment, and observability — the full integration surface of a modern AI system.
API & Service Integration
- Design REST, GraphQL, and gRPC interfaces around LLM and ML services
- Integrate Generative AI APIs (OpenAI, Anthropic, Google, Azure, Bedrock)
- Wire AI capabilities into existing enterprise applications safely
Data Pipelines & Orchestration
- Build streaming and batch pipelines for embeddings, retrieval, and training
- Orchestrate event-driven workflows with Kafka, queues, and schedulers
- Implement RAG pipelines with chunking, indexing, and hybrid search
Agent & Tool Wiring
- Connect agents to internal tools, APIs, and knowledge bases
- Implement tool-calling, function calling, and structured outputs
- Govern agent permissions, sandboxing, and audit trails
MLOps & Deployment
- Containerize and deploy AI services on Kubernetes and serverless
- Automate CI/CD with model versioning and canary releases
- Manage feature flags, blue-green rollouts, and rollbacks
Observability & Reliability
- Instrument prompt traces, latency, token cost, and error rates
- Build dashboards, SLOs, and alerts for AI workloads
- Run chaos drills and incident response playbooks for AI systems
Security & Compliance
- Apply secrets management, PII redaction, and policy enforcement
- Implement guardrails, rate limits, and abuse protection
- Align integrations with SOC 2, HIPAA, GDPR, and ISO 27001 needs
Skills & expertise
Backend depth meets AI fluency
Engineers who can architect distributed systems, integrate LLM providers, run production RAG, and operate it all with the discipline of a senior platform engineer.
- AI / LLM integration
- RAG architecture
- Vector database engineering
- Event-driven architecture
- Microservices & APIs
- MLOps & CI/CD
- Cloud platforms (AWS / Azure / GCP)
- Container orchestration
- Observability & SRE
- Security & compliance
- Performance & cost tuning
- Python & TypeScript
AI tools & stack we operate in
Hands-on across the full AI integration stack — providers, frameworks, data, vector stores, and DevOps.
AI / LLM Providers
- OpenAI
- Anthropic Claude
- Google Gemini
- Azure OpenAI
- AWS Bedrock
- Hugging Face
Frameworks & Agents
- LangChain
- LlamaIndex
- LangGraph
- CrewAI
- AutoGen
- Vercel AI SDK
Languages & Backend
- Python
- FastAPI
- Node.js
- TypeScript
- Go
- gRPC / GraphQL
Data & Streaming
- Kafka
- Pulsar
- Airflow
- Dagster
- Snowflake
- Databricks
Vector & Storage
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Redis
- Elasticsearch
DevOps & Observability
- Kubernetes
- Docker
- Terraform
- Prometheus
- Grafana
- Langfuse
Engagement models
Hire on terms that match your stage
Dedicated Full-time
Senior AI Integration Engineer embedded full-time to own AI infra end-to-end.
- 40 hrs / week
- Direct ownership
- Long-term roadmap
Part-time / Fractional
Strategic 20 hrs/week coverage for architecture, MLOps, and reliability reviews.
- Architecture focus
- Code reviews
- Flexible cadence
Project / Outcome-based
Fixed-scope engagement to ship a defined integration, RAG pipeline, or AI deployment.
- Clear deliverables
- Milestone billing
- Predictable timeline
How to hire
From first call to first sprint in under a week
02
Shortlist in 48 hours
Receive 2–3 vetted AI Integration Engineers with relevant case studies.
03
Interview & evaluate
Run technical interviews, system design rounds, and optional paid trial tasks.
04
Onboard in 7 days
Sign, kick off, and start delivery — backed by a 14-day risk-free trial.
FAQs
Hiring an AI Integration Engineer — questions we hear most
What does an AI Integration Engineer do?+
An AI Integration Engineer connects AI capabilities — LLMs, agents, vector databases, and ML models — to the rest of your stack via reliable APIs, event-driven pipelines, and MLOps. They make AI features production-ready, observable, and secure.
How is this role different from a backend engineer?+
AI Integration Engineers combine backend skills with deep familiarity with LLM behavior, prompt orchestration, RAG, vector search, agents, evaluation, cost optimization, and AI-specific observability. They handle both deterministic services and probabilistic model outputs.
Can you integrate AI into our existing legacy systems?+
Yes. Our engineers regularly integrate AI with legacy ERPs, CRMs, on-prem databases, and custom internal APIs using adapters, queues, and middleware — without forcing a rewrite of core systems.
How do you handle reliability and cost for LLM features?+
We use techniques such as model routing, response caching, batching, rate-limiting, and async orchestration, plus full token-level observability to keep latency and spend predictable at scale.
Do you support cloud-native and hybrid deployments?+
Yes. We deploy AI services across AWS, Azure, GCP, and hybrid environments using Kubernetes, Terraform, and managed AI platforms based on your security and data residency needs.
How quickly can the engineer start?+
Most engagements start within 7 days of contract signing. Urgent needs can be accelerated to 72 hours.
Ready to hire your AI Integration Engineer?
Share your AI integration goals and we'll send a shortlist of pre-vetted engineers within 48 hours — backed by a 14-day risk-free trial.
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