Ava Developer

TEKsystems
2 days ago

Role details

Contract type
Permanent contract
Employment type
Part-time (≤ 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Tech stack

Java
Amazon Web Services (AWS)
Azure
Cloud Computing
Code Generation
Databases
Continuous Integration
DevOps
Github
JSON
PostgreSQL
Performance Tuning
Role-Based Access Control
Redis
Software Engineering
Data Streaming
Management of Software Versions
Data Logging
React
Large Language Models
Multi-Agent Systems
Prompt Engineering
Spring-boot
Gitlab-ci
Kubernetes
Kafka
Virtual Agents
REST
Terraform
Jenkins

Job description

Software Engineering & Delivery:

  • Design, Build, and ship production quality services within our current business controls
  • Build and Optimise DecOps pipelines - enhancing CI/CD processing, including testing and platform/process observability
  • Design, iterate, and optimise prompts for a wide range of LLM use cases - instruction following, structured output generation, classification, summarisation, code generation, and reasoning tasks
  • Build and manage prompt libraries and versioning strategies, treating prompts as first-class engineering artefacts
  • Collaborate with product and domain teams to translate business requirements into precise, well-structured prompt specifications

Agentic AI & LLM Integration

  • Architect and implement/maintain agentic systems - autonomous agents, multi-agent pipelines, tool-use workflows, planning loops, and memory-augmented reasoning
  • Implement LLM observability - logging, tracing, evaluation pipelines, and guardrails - to maintain quality and reliability in production AI systems
  • Advise on responsible AI deployment, including output validation, human-in-the-loop design, and risk mitigation for agentic workflows

Technical Skills & Experience

Full-Stack Engineering

Requirements

Strong proficiency in Java/Spring Boot - REST APIs, event-driven services, security, and performance optimisation

Solid React development skills and API integration

Experience with relational and in memory databases (PostgreSQL, Redis)

Messaging and streaming platforms - Kafka, or equivalent

Prompt Engineering

Demonstrable, hands-on Experience engineering prompts for production LLM applications - not just prototypes

Familiarity with prompt evaluation frameworks and tooling (PromptFlow, LangSmith, Braintrust, or custom eval pipelines)

Understanding of tokenisation, context limits, temperature tuning, and model-specific behaviours across major LLM providers

Experience structuring prompts for tool/function calling, structured JSON output, and multi-turn conversations

DevOps & Infrastructure

Kubernetes - deployments, services, ingress, Helm, Kustomize, HPA, and RBAC

CI/CD pipelines - GitHub Actions, GitLab CI, Jenkins, or equivalent

Infrastructure as code - Terraform

Cloud platforms - AWS, GCP, or Azure

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