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:
Str