Develop future AI middleware and agent platforms for PlayStation IT, designing and delivering intelligent agents that operate across enterprise domains.
Requirements
- Proficiency in Python or TypeScript (ideally both) with strong software-engineering fundamentals (testing, design patterns, observability, performance tuning).
- Practical knowledge of ML/GenAI concepts: embeddings, vector search, prompt engineering, fine-tuning, and evaluation metrics.
- Solid understanding of workflow orchestration frameworks for LLM/GenAI pipelines.
- Hands-on experience with data-pipeline tooling and batch/stream ETL.
- Solid understanding of cloud platforms (AWS preferred), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, CDK).
- LLM tooling: LangChain, LangGraph, LlamaIndex, Hugging Face Transformers, OpenAI or Anthropic APIs
- Data stores: Postgres, DynamoDB, Redis, and vector databases such as Pinecone, Weaviate, FAISS
Responsibilities
- Architect and Operate AI Middleware: Design, implement, and manage Model Context Protocol (MCP) servers, gateways, and API wrappers that securely expose enterprise systems, tools, and data for AI agent consumption.
- Build and Govern Agentic Workflows: Deploy and extend agentic AI platforms (e.g., LangGraph, LangChain) to deliver resilient orchestration, enforcing governance, tracing, audit logs, and human-in-the-loop controls.
- Develop Enterprise Data & Context Pipelines: Build scalable pipelines for parsing, cleaning, embedding, and storing structured and unstructured enterprise data (text, code, multimedia) to enable retrieval-augmented generation and domain-specific agent knowledge.
- Productize and Optimize AI Services: Translate prototypes into production-grade microservices (Python/TypeScript, Kubernetes, CI/CD), optimizing for latency, throughput, resilience, and token/cost efficiency.
- Ensure Security, Compliance, and Reliability: Engineer AI middleware and workflows that meet enterprise standards for safety, governance, compliance, and fault tolerance, with built-in monitoring, telemetry, and automated evaluation harnesses.
- Lead, Collaborate, and Mentor: Partner multi-functionally with architects, platform teams, and product owners to deliver business-aligned AI solutions, while mentoring engineers and staying at the forefront of emerging LLM and middleware trends.
Other
- More than 10 years in software engineering, with a minimum of 3 years involved in building ML-, data-, or LLM-centric systems.
- Excellent communication skills and a proven ability to collaborate across teams in a fast-paced environment.
- This is a hands-on senior engineering role that requires a strong bias for action, an agile attitude, and the ability to change directions quickly as the AI landscape evolves.
- Bonus: proficiency in Go, experience with multimodal or edge inference, contributions to GenAI open-source projects
