Hire AI/ML engineers for Marketplace in São Paulo.

Search-intent framing

Buyers searching 'hire' are typically ready to commit headcount or capacity right now — board-approved budget, board-pressured timeline, an open seat or an understaffed lane that needs to be productive this quarter. The hiring pipeline has either stalled at the senior level or the CTO has decided that velocity matters more than headcount permanence and wants a path that delivers production-grade output within days, not months.

Buyer mindset

Hire-intent CXOs care about ramped output by week two, not vendor pitch decks. The pod retainer model collapses the 6-month FTE hiring loop into a 7-day discover-trial-deploy cycle without sacrificing senior-grade delivery. At $2,500/month for an embedded engineer or $15/hour for hourly engagements, the total loaded cost runs 40–60% below a comparable metro FTE when you factor in benefits, equity, recruiter fees, and ramp-up productivity loss.

Common use cases

AI/ML pods typically ship LLM-powered application backends including RAG pipelines with hybrid search (semantic plus keyword retrieval), agentic systems with tool-calling and multi-step reasoning loops, vector-database integrations with chunking strategy design and embedding pipeline optimisation, model fine-tuning workflows using LoRA and QLoRA on domain-specific datasets, evaluation harnesses with automated regression detection and golden-dataset management, production inference services with GPU autoscaling and per-request cost monitoring, and AI-native product features like document analysis, conversation summarisation, code generation, and intelligent search. Devlyn engineers ship AI/ML with LangChain or LlamaIndex for orchestration, vector stores (Pinecone, Weaviate, pgvector, Qdrant) for retrieval, multi-provider model routing across OpenAI, Anthropic, Cohere, and open-source models via vLLM, and guardrails infrastructure for output safety and hallucination mitigation.

AI-augmented angle

AI-augmented AI/ML workflows lean on Cursor and Claude Code for evaluation-harness scaffolding with golden-dataset management and assertion frameworks, prompt-version management with A/B rollout infrastructure and rollback safety, deterministic test wrapping of stochastic systems using seed-controlled and assertion-bounded strategies, RAG pipeline configuration with chunking-strategy tuning and retrieval-quality metrics, and API endpoint scaffolding for inference services — all under senior validation that owns architecture decisions, model-provider selection based on quality-cost-latency tradeoffs, inference-cost review tracking token spend per user session, guardrails and safety-filter design, and the increasingly critical AI compliance posture covering EU AI Act risk classification, NIST AI RMF, and model-card disclosure obligations. Compression shows up strongest in evaluation harness buildout, retrieval-pipeline configuration, and inference-endpoint scaffolding.

Engagement shape

AI/ML engagements at Devlyn typically run as one senior ML engineer plus shared backend infrastructure for $5,500–$10,000/month, covering RAG pipeline architecture, model integration, and evaluation harness design. This scales to a two- or three-engineer pod when the roadmap splits across model training and fine-tuning (GPU compute management, dataset curation, training-run orchestration), production inference serving (autoscaling, model-version routing, latency optimisation), and evaluation and safety-testing (prompt regression suites, adversarial testing, compliance posture). The pod structure is especially critical in AI/ML where training, serving, and evaluation workflows have fundamentally different compute profiles and deployment cadences.

Ecosystem fluency

AI/ML ecosystem depth covers the full modern surface: LangChain for agent and chain orchestration with tool-calling, LlamaIndex for data-connector-rich RAG with hybrid search, Pinecone and Weaviate for managed vector search, pgvector for Postgres-native embedding storage, Qdrant for self-hosted high-performance vector search, OpenAI and Anthropic APIs for frontier models, Cohere for embeddings and reranking, Hugging Face Transformers and Inference API for open-source models, vLLM and Ollama for self-hosted inference, Ragas and Promptfoo for RAG and prompt evaluation, Modal and Replicate for serverless GPU compute, Weights and Biases for experiment tracking, and Cloudflare Workers AI for edge inference. Devlyn engineers operate fluently across this entire surface with production-hardened patterns for cost monitoring, quality evaluation, and safety guardrails.

Compliance posture

Marketplace engagements navigate sales-tax compliance across jurisdictions following the Wayfair v South Dakota nexus framework, 1099-K reporting obligations for seller payouts with IRS threshold tracking, KYC and AML requirements for payment flows including identity verification for high-volume sellers, platform-liability considerations under DSA for EU marketplaces and Section 230 for US platforms, and increasingly algorithmic-transparency obligations for search-ranking and recommendation systems. Devlyn pods include security review on payment escrow, seller-identity verification, and trust-and-safety automation as standard engagement practice.

Common architectures

Two-sided onboarding flows for buyers and sellers with distinct verification requirements, payment escrow with platform-fee collection through Stripe Connect or Adyen for Platforms, search and ranking with relevance tuning and A/B-testable algorithm variants, dispute resolution workflows with evidence collection and automated-mediation rules, fraud-detection systems with behavioural scoring and account-suspension automation, trust-and-safety pipelines with content moderation and policy-enforcement queues, and review and rating systems with fraud-resistant verification. Pods working marketplace roadmaps pair backend depth with search-ranking, fraud-detection, and payment-integration specialists.

Typical CTO constraints

Marketplace CTOs are usually constrained by chicken-and-egg supply-demand dynamics where platform value depends on both sides growing in parallel, fraud rates that increase with marketplace scale and can erode buyer trust rapidly, and the velocity gap between trust-and-safety incidents and platform response time. Additional pressure comes from payment-compliance obligations that scale with transaction volume and seller count. Pod retainers compress engineering velocity around trust-and-safety posture and payment-compliance readiness.

São Paulo talent pool

São Paulo engineering carries Latin America's largest fintech (Nubank, PagSeguro, Stone), B2B SaaS, and e-commerce depth. Senior backend FTE base salaries run BRL 180K–360K (~$36K–$72K) with strong Portuguese-English bilingual team capability.

Engineering culture in São Paulo

São Paulo engineering culture is fintech-anchored (Nubank gravity), product-led, and Latin-America-regional-scale-aware. Pods serving São Paulo teams typically need Banco Central do Brasil and BACEN compliance awareness.

Time-zone alignment

Devlyn pods deliver 7+ hours of daily overlap with São Paulo business hours, with sync architecture calls scheduled morning BRT to align with fintech, e-commerce, and Brazil-anchored Latin-America calendars.

São Paulo hiring climate

São Paulo FTE pipelines run 2–4 months for senior backend roles. Compensation gravity from Nubank and Mercado Libre regional offices elongates the funnel. Pod retainers compress the calendar at Brazilian-startup-friendly economics.

Dominant verticals: fintech, e-commerce, B2B SaaS, marketplace, healthtech