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Hammerjack Pty Ltd

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Java and Python AI Developer

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Summary

Leads a team of offshore engineers delivering AI initiatives: builds Java Spring Boot and Python (Flask/FastAPI/Django) backend microservices, React/Angular frontends, and production-grade GenAI features (LLMs, RAG, agentic AI, MCP, guardrails) on cloud/DevOps infrastructure.

We are seeking an engineer who is exceptional in Java Springboot, experienced with web frameworks (Flask, FastAPI, Django), comfortable building frontends (React, Angular), and capable of delivering production-grade GenAI solutions (LLMs, RAG, Agents/Agentic AI, MCP, guardrails). Candidate should be able to design, build, and operate services and applications that leverage modern AI patterns with strong software craftsmanship.

Responsibilities

  • Lead a team of Offshore Engineers and deliver successful iterations of AI initiatives.
  • Build backend services in Springboot, Python using Flask, FastAPI, or Django. design clean APIs, background jobs, and integrations.
  • Develop user interfaces with React or Angular; collaborate on UX and component libraries; ensure accessibility and performance.
  • Own quality with PyTest (including fixtures, parametrization, coverage), integration tests, and contract tests; enable CI test automation.
  • Ship GenAI features: design prompts, tools, memories, and workflows, implement RAG pipelines, orchestrate agentic systems.
  • Productionize AI with guardrails for safety, compliance, observability, and fallback strategies; measure quality (latency, cost, accuracy).
  • Work across data layers: vector stores, embeddings, caching, and secure connectors; uphold data privacy and governance.
  • Collaborate with product, design, and platform teams, review code, architect solutions, document decisions, and mentor peers.

KEY SKILLS

Non-negotiable/Required

  • 10+ years of experience in design, development, and triaging for large, complex systems.
  • Experience in Java and object oriented design skills
  • 5+ years of microservices development
  • 3+ years working in Springboot
  • Experienced using API dev tools like Intellij/Eclipse, Postment, Git, Cucumber
  • Hands on experience in building microservices based application using Springboot and REST, JSON
  • DevOps understanding – containers, cloud, automation, security, configuration management, CI/CD experience in streaming technologies like Apache Kafka.
  • Gen AI
  • Java Springboot Microservices
  • Python & Backend 5+ years of experience
  • Expert-level springboot and Python (typing, async, packaging, linting: black/ruff/flake8, performance profiling).
  • Web frameworks: Flask, FastAPI, Django (routing, middleware, ORM, auth, background tasks).
  • API design (REST/JSON), OpenAPI/Swagger, pagination, idempotency; secure patterns (OAuth/OIDC, JWT, RBAC).
  • Frontend 3+ years of experience
  • React or Angular: component design, state management, routing, forms, accessibility (WCAG), unit/e2e tests (Jest, Playwright).
  • Build tooling: Vite/Webpack, npm/yarn
  • GenAI / Agentic AI 1 year of experience
  • LLM concepts: tokenization, context windows, embeddings, temperature/top-p, system prompts, tool/function calling.
  • RAG: ingestion pipelines, chunking strategies, metadata, types of RAG (basic, hierarchical, hybrid, multi-vector, agent-routed), evaluators.
  • Agents & Agentic AI: planning/execution loops, tool orchestration, memory, multi-agent collaboration, error handling.
  • MCP (Model Context Protocol): designing tools/resources, host applications, capability negotiation, secure tool exposure.
  • Guardrails: input/output filtering, policy enforcement, prompt-injection resilience, PII controls, jailbreak detection, red-teaming.
  • Open-source frameworks: CrewAI, LangGraph/LangChain (nodes/edges, executors, runnables, toolkits). Familiarity with alternatives (Haystack, LlamaIndex) is a plus.

Data & Infra 3-5 years of experience

  • Vector DBs, Relational DBs (PostgreSQL, MySQL) and Caching (Redis).
  • Cloud & DevOps: containers (Docker), orchestration (Kubernetes), CI/CD, secrets management; monitoring/observability (logs, traces, metrics).
  • Performance & cost management for LLM workloads. Batch vs. streaming jobs. Queuing (MQ, Kafka).

Nice-to-have/Advantage

  • LLM Observability, Guardrails, Vector database, Graph Database

Skills

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See also

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