Senior Backend AI Engineer

The candidate will have responsibilities across the following functions:

You'll design and build systems that:

• Ingest and process high-volume, multi-source data across enterprise systems.
• Validate stateful business workflows spanning loosely coupled services.
• Reconcile data across sources with consistency and correctness guarantees.
• Build event-driven, asynchronous pipelines for real-time and batch validation.
• Integrate with complex external systems (e. g., SAP, APIs, data warehouses).

You'll also work on:

• Deploying LLM-powered agents into production workflows.
• Designing systems to evaluate, monitor, and improve agent behaviour over time.
• Building safeguards for correctness, reliability, and failure handling.
• Ensuring non-deterministic AI outputs meet deterministic system requirements.
• You'll own problems end-to-end: design → build → operate.

Requirements:

• Production builder: you've shipped LLM-powered features real users depend on and debugged them when they broke.
• LLM practitioner: you understand hallucinations, retrieval failures, context limits, and what it takes to make agents deterministic enough for enterprise use.
• Systems thinker: you design for latency, failure modes, retry logic, and observability before features.
• Enterprise-aware: data residency, compliance, audit trails, and deterministic guardrails are first-class design constraints for you.

Background That Maps Well:

• 3+ years in AI/ML or backend engineering with strong AI exposure.
• Hands-on production experience with LLM APIs (Anthropic, OpenAI, Cohere).
• Experience designing evaluation frameworks: automated evals, regression tests, or LLM-as-judge pipelines.
• Strong Python; experience with LangChain, LlamaIndex, or similar agentic frameworks.
• Familiarity with RAG architectures: chunking, embedding models, vector DBs, retrieval quality.

Tech (evolving):

• Languages: Python, Go, Node.js .
• Data: Snowflake, Databricks, Postgres.
• Architecture: distributed systems, event-driven pipelines.
• Infra: containers + cloud platforms (Kubernetes / ECS / Cloud Run).
• AI: LLM-based agents with evaluation, monitoring, and control systems.