AI & Machine Learning
Applied AI engineers who ship production-grade GenAI features. from RAG and agents to evals, guardrails and cost-optimized inference.
Who you get on day one
Applied AI engineers who ship eval-driven, cost-aware GenAI features into production.
- AWS ML Specialty
- GCP ML Engineer
- DeepLearning.AI specializations
- Builds production agents with guardrails and tracing
- Designs eval harnesses for LLM features
- Optimizes inference cost via routing and caching
Strategies & playbooks for AI & Machine Learning
Concrete plays our consultants run to resolve the complex problems we see most often in this discipline.
Teams ship LLM features without measuring quality. silent regressions in prod.
Build an eval harness (golden set + LLM-as-judge + human review) before prompt iteration; gate releases on eval scores.
Confident model rollouts; regressions caught pre-prod.
First-pass RAG hallucinates and retrieves irrelevant chunks.
Hybrid retrieval (BM25 + vectors), re-ranking, query rewriting, citation-required prompts, and per-doc access controls.
Answer quality jumps; trust and adoption follow.
Inference bill scales linearly with usage; p95 latency too high.
Tiered model routing (small models for easy queries, big for hard), prompt caching, semantic caching, and streaming.
30 to 70% cost reduction with maintained quality.
Agents go off the rails. wrong tools, infinite loops, prompt injection.
Tool allow-lists, max-step budgets, output schemas, input/output guardrails, full tracing.
Production-safe agents with observable behavior.
How AI accelerates AI & Machine Learning
We build with the same AI tooling we deploy. every consultant operates LLMs daily as engineer and as user.
Multi-model routing across OpenAI, Anthropic, Google and OSS models per task profile.
Production agents with tracing, evals and human-in-the-loop checkpoints.
Continuous eval pipelines and trace inspection for every prompt change.
Recommended tools we propose as consultants
Curated stack our consultants bring on day one. chosen for fit with your scale, team and existing investment.
- GPT-5 / Claude / Gemini 2.5 ProFrontier reasoning and multimodal.
- Llama / MistralSelf-hosted when data residency or cost demands it.
- PgvectorVector search inside Postgres. simplest ops story.
- Qdrant / WeaviateDedicated vector DBs for high-scale retrieval.
- LangSmithTracing + evals for LLM apps.
- Modal / ReplicateServerless GPU inference.
What this discipline really is
AI & Machine Learning at Codivers spans applied GenAI (RAG, agents, fine-tuning) and traditional ML (forecasting, classification, recommenders). The hard parts are rarely the models. they’re evaluation, data, cost control and integrating safely into real workflows.
Key areas inside AI & Machine Learning
RAG, agents, structured outputs, function calling. applied to real product surfaces.
Eval harnesses, regression suites, guardrails, hallucination detection, red-teaming.
Model registry, feature store, training pipelines, monitoring drift and performance.
Forecasting, classification, recommenders, anomaly detection. often the right answer.
Caching, model routing, distillation, prompt compression and budget alerts.
Maturity model. where are you today?
POCs in notebooks, no evals, prompts in code comments.
Some prompts versioned, manual evals, basic monitoring.
Eval harness in CI, guardrails, cost dashboards, structured outputs.
Continuous evals, automatic regression gates, model routing, model risk management.
Best practices we apply
- No evals = no AI feature in production. Period.
- Track cost per request and per feature; alert on regressions like you do for latency.
- Use structured outputs (JSON schemas) wherever the downstream consumer is code.
- Treat prompts and tools as code. versioned, reviewed, tested.
- Start with the smallest model that meets the eval bar; scale up only with evidence.
Common pitfalls & how we fix them
Outcomes you can expect
- Production-grade GenAI features
- Eval-driven model rollouts
- Cost-optimized inference
- Safe, monitored deployments
Engagement models
KPIs we commit to
Tools & technologies
What you get
- GenAI feature design with guardrails
- RAG pipeline with eval harness
- Cost & latency budget per feature
- Model monitoring (drift, hallucination, PII)
- Fine-tuning / RLHF where justified
- MLOps platform for training & serving
How we deliver
- 1DiscoveryWorkshops to scope outcomes, constraints, success metrics and risks.
- 2MatchRanked consultants with score, availability and pre-vetted skills.
- 3Pre-onboardingStack simulation aligns the consultant with your conventions before day one.
- 4DeliveryTwo-week cadence with transparent metrics, demos and async updates.
- 5Knowledge transferDocumentation, runbooks and pairing so capability stays in-house.
Roles available on the bench
| Role | Level | Indicative rate |
|---|---|---|
| Applied AI Engineer | Senior | From €750/day |
| ML Engineer | Senior | From €750/day |
| AI Architect | Staff | From €950/day |
Rates are indicative; final pricing depends on seniority, location and engagement length.
Common stack overlap
Certifications on the bench
- AWS ML Specialty
- GCP ML Engineer
- Hugging Face Certified
Support automation with RAG + agents
60% of support tickets were repetitive, response time averaged 8h.
RAG over knowledge base + agent workflow with tool use, deployed with eval gates and PII filters.
Auto-resolved 47% of tickets, response time down to 12 min, CSAT held steady.