AI Agentic integration Engineer (Senior / Lead)

Vallettasoftware - custom mobile/web software developer in the US and Europe. Our teams implement IT projects of varying complexity, including website and mobile app development, enterprise systems, and solutions based on artificial intelligence and machine learning (AI/ML).

Our company has earned a place among Clutch's Fall 2025 Champions. These awards confirm that we are a Top 15 AI Agent Developing companies!

We are a distributed team - you can work from any place in the world, except RF and RB, ensuring silence, good internet connection, availability and proper environment .

We are looking for a Senior / Lead AI Engineer to build production-ready AI systems where:

• LLM is the core layer of the solution

• Agentic workflows are used as the primary orchestration pattern

• System quality is managed through evaluation

• Reliability, observability, and cost control are designed as part of the architecture, not added later

This is not a backend engineer with AI functions, nor is it a prompt engineer.

This is an engineer who knows how to build AI-native systems end-to-end and take them all the way to production.

1. Hard Requirements

1.1. AI / LLM Systems

Must-have:

• Real experience developing and shipping production LLM systems

• Experience working with LLM APIs: OpenAI / Anthropic / Gemini / similar

• Prompt design

• Structured outputs

• Tool / function calling

• Model selection and understanding of trade-offs between models

1.2. Agentic Systems

Must-have:

• Experience designing multi-step workflows

• Experience developing agent-based systems: single-agent and/or multi-agent

• Orchestration: planning, execution, retry, fallback, verification

• Management of:

  • State

  • Context

  • Memory

• Understanding when an agentic approach is needed and when it's not

• Understanding of trust boundaries in agentic systems

• Principle of least privilege for tool permissions

• Protection against indirect prompt injection via external data (retrieval, tool results, external APIs)

1.3. Evaluation & Quality Control

Must-have:

• Building evaluation pipelines

• Offline evaluation

• Comparing prompt / model versions

• Quality metrics

• Approaches to online validation / A/B testing / human review loops

• Ability to connect evaluation to real product quality

1.4. Context Management & Hallucination Control

Must-have:

• Context management:

  • Chunking strategies

  • Context window optimization

  • Memory patterns

  • Retrieval scope control

• Hallucination reduction techniques:

  • Grounding

  • Retrieval

  • Tool-based verification

  • Constraints

  • Self-check / validation patterns

1.5. Production / LLM Ops / Reliability

Must-have:

• Retries / exponential backoff

• Timeout handling

• Fallbacks / model routing

• Degraded mode / graceful failure

• Rate-limit handling

• Observability:

  • Latency

  • Token usage

  • Cost

  • Failure rate

  • Output quality signals

• Cost control

• Monitoring and debugging AI systems in production

• PII handling: filtering before logging, tenant isolation in memory and retrieval

• Output validation and content guardrails

• Awareness of data residency risks when using external LLM APIs

1.6. Data / Retrieval

Must-have:

• Understanding of retrieval pipelines:

  • Embeddings

  • Chunking

  • Reranking

  • Retrieval quality tuning

• Experience with vector storage / vector DB of any type

• Working with structured and unstructured data

1.7. Engineering Foundation

Must-have:

• Strong engineering background in backend / system development

• Backend stack is not critical

• Ability to build APIs, services, integrations, async workflows

• SQL + NoSQL

• Git, Docker, CI/CD

Nice-to-have:

• Full-stack development experience (backend + frontend)

• Understanding of UI/UX aspects of AI products (chat, copilots, dashboards)

1.8. AI Safety & Security

Must-have (awareness level for Senior, ownership for Lead):

Prompt Injection:

• Understanding the difference between direct and indirect injection

• Indirect injection — a specific threat to agentic systems: attacks via data from retrieval, tool results, external sources

• Content sanitization before inserting into context

• Architectural separation of system prompt, user input, and external data

Tool Permission Model:

• Principle of least privilege: minimum necessary permissions for each agent and tool

• Separation of read-only and write operations at the architecture level, not via prompts

• Human-in-the-loop for irreversible actions (delete, send, publish, execute code)

• Whitelist of allowed external calls

Data Leakage & PII:

• Tenant isolation: one user's data never enters another user's context

• PII masking before logging (including tool results and retrieval results)

• Understanding that LLM APIs are third-party; for regulated domains — DPA, filtering, or self-hosted

Output Safety:

• Output validation: schema checks, content filtering

• Understanding the difference between "the prompt says don't do X" (weak protection) and "the architecture does not allow X" (strong protection)

1.9. Communication

Must-have:

• English B2+

• Ability to explain architectural decisions, trade-offs, and risks

• For Lead: ability to set engineering standards and lead the technical direction of the team