We’re looking for a Senior Software Engineer (AI Transformation) who is an engineer first: someone with deep software engineering fundamentals, backend system design skills, and practical experience integrating modern AI technologies into production systems to solve tangible business problems.
This role requires the ability to design robust APIs, distributed background workflows, and resilient data architectures, while leveraging LLMs, agentic patterns, and RAG pipelines responsibly—knowing when AI is the right answer and when deterministic software engineering is superior.
Key Responsibilities

• End-to-End Service Delivery: Design, develop, test, deploy, and maintain production-grade backend services and internal applications from initial problem discovery to continuous production operation.
• Workflow Automation & AI Agents: Identify high-friction business operations and build intelligent workflows, automations, and multi-step agents using tool calling, structured outputs, and agent orchestration.
• Architecture & Distributed Systems: Architect backend services that balance performance, scalability, latency, cost, and reliability—implementing asynchronous event queues, background workers, and clean integration boundaries.
• RAG & Knowledge Integration: Build and optimize retrieval-augmented generation (RAG) pipelines, semantic vector search, chunking strategies, and grounding mechanisms connected to enterprise data stores.
• Reliability, Security & Guardrails: Institute defensive engineering practices against hallucinations, prompt injection, unauthorized tool execution, and non-deterministic failures using structured validation, tracing, and human-in-the-loop review.
• Evaluation & Optimization: Continuously evaluate AI solutions against task-specific benchmarks, regression datasets, token usage, latency, and operational cost.
• Cross-Functional Collaboration: Partner with product, data, infrastructure, and business stakeholders to translate ambiguous operational challenges into clear, maintainable technical solutions.
• Engineering Standards & AI-Assisted Workflows: Establish reusable architectural patterns, CI/CD practices, and documentation, utilizing AI-assisted developer tooling effectively without compromising code quality or security.

Qualifications & Requirements

• Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.
• 4+ years of professional software engineering experience with strong fundamentals in data structures, algorithms, modular system design, and clean code practices.
• Proficiency in at least one modern backend language and ecosystem (Python, TypeScript/JavaScript, or Go), along with frameworks such as FastAPI, Django, NestJS, Express, or Go web frameworks.
• Solid experience in database modeling, relational transactions, indexing, and query optimization (e.g., PostgreSQL).
• Practical experience with asynchronous processing, background workers, caching, and message brokers (Redis, Kafka, RabbitMQ).
• Hands-on experience integrating LLM APIs, prompt/context engineering, structured JSON outputs, function/tool calling, and schema validation.
• Working knowledge of agentic patterns, state management, and orchestration frameworks (such as Mastra, LangGraph, CrewAI, or OpenAI Agents SDK).
• Familiarity with Docker, Linux environments, automated testing suites, Git workflows, and CI/CD pipelines.

Preferred Qualifications (Nice to Have)

• Direct experience deploying, tracing, and observing LLM-powered applications in live production environments.
• Hands-on experience with vector search and vector stores (e.g., pgvector, Qdrant, Elasticsearch).
• Familiarity with the Model Context Protocol (MCP) and connecting agents to external tooling and platforms.
• Experience with cloud platforms, container orchestration (Kubernetes), and infrastructure as code.
• Expertise in LLM cost optimization (prompt caching, model routing, token budgeting, and asynchronous batching).
• Experience architecting internal developer tools, enterprise workflow engines, or B2B productivity platforms.

What Makes This Role Unique
This is not a research lab or a prompt-engineering demo sandbox; it is a core software engineering role where AI is treated as a powerful component in production systems. You will operate with end-to-end technical autonomy to eliminate business friction, define how AI-augmented applications are built and monitored across the company, and bridge the gap between emerging AI capabilities and dependable enterprise software.