Senior Software Engineer - AI

You will take ambiguous customer and product problems, evaluate different approaches, and independently turn the strongest solution into reliable, secure, observable, andcost-efficientproduction systems. This is an applied engineering role focused on shipping real AI products.

Who you are & what you’ll need to succeed

• Translate loosely defined product problems into practical AI solutions, choosing between LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software.

Design and build production AI services using Python,FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.

Build reliable tool calling agents andmulti stepworkflows that interact with internal APIs, MCP tools, business systems, and knowledge sources.

Design event driven systems using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including retries, dead letter handling, idempotency, back pressure, and failure recovery.

Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations.

Experiment with models from OpenAI, Anthropic, Google,xAI, and open weight ecosystems, selecting the right model for quality, latency, reliability, and cost.

Create AI evaluation pipelines using curated datasets, regression tests, retrieval metrics, LLM as judge techniques,groundednesschecks, and tool execution evaluation.

Diagnose hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs.

Operate as an AI powered engineer using Cursor, Claude Code, OpenAI Codex, or equivalent coding agents to accelerate design, implementation, testing, debugging, and refactoring.

Whatwe’relooking for

5 or more years of professional software engineering experience building production backend, distributed, orcloud basedsystems.

Hands on experience building Applied AI, LLM, RAG, NLP, oragent basedapplications, with meaningful production exposure.

Advanced Python skills includingFastAPI, asynchronous programming,Pydantic,SQLAlchemyorSQLModel, andproductionAPI development.

Strong backend and distributed systems fundamentals including REST APIs, concurrency, background processing, caching, reliability, and production debugging.

Production experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures.

Hands onexperience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows.

Experience building at least one RAG or knowledge grounded system with measurable quality and latency outcomes.

Strong PostgreSQL and data modeling skills, with experience using Redis,pgvector, vector databases, or hybrid search technologies.

Demonstrated use of AI coding agents such as Cursor, Claude Code, Codex, or equivalent tools as a core part of daily software engineering.

Nice to have.

You should be comfortable going beyond a working demo andunderstandhow modern AI systems behave, fail, and scale in production.

Strong understanding of prompting, structured outputs, tool schemas, context management, model routing, retries, fallbacks, rate limits, and streaming responses.

Ability to design experiments and evaluations that compare approaches using measurable outcomes rather than subjective testing alone.

Understanding of retrieval quality, grounding, hallucination mitigation, prompt injection risks, tool authorization, and practical agent guardrails.

Experience with Docker, CI/CD, automated testing, observability, distributed tracing,OpenTelemetry,Langfuse, or similar tooling.

Howyou’llwork

You will often start with an ambiguous customer or product goal rather than a detailed implementation plan. You should be able to independently break the problem down,validateassumptions, choose an approach, and drive the solution through production.

Turn product goals into testable technical hypotheses and rapidly prototype alternatives when needed.

Choose the simplest architecture that solves the problem well rather than using an agent or LLM by default.

Define quality, latency, reliability, safety, and cost targets and measure production performance against them.

Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support.

Use AI coding agents aggressively to increase engineering velocity while preserving architecture quality, testing discipline, security, and maintainability.

What will make you stand out

Experience with Semantic Kernel, Microsoft Agent Framework,LangGraph,PydanticAI, or equivalent agent frameworks.

Experience building or consuming Model Context Protocol tools and servers, or working with model gateways such asLiteLLM.

Experience with Azure AI Search,pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI platform technologies.

Experience buildinghigh scalecustomer facing AI products, open weight model inference, AI safety controls, or automated agent evaluation systems.

Strong problem solving, excellent engineering judgment, and the ability to independently move from ambiguity to a production quality solution are the foundation for this role.

Join us to build the agent-powered backbone of our AI platform—robust, model agnostic, and ready for millions of users.

This Job Description includes the essential job functions required to perform the job described above, as well as additional duties and responsibilities. This Job Description is not an exhaustive list of all functions that the employee performing this job may be required to perform. The Company reserves the right to revise the Job Description at any time, and to require the employee to perform functions in addition to those listed above.