Hire Prefect Developers
Hire nearshore Prefect developers from Latin America in 5 days, at a fraction of US costs. Build your dream team while saving up to 60%, without compromising on quality or timezone compatibility.
What Makes Tecla Different for Hiring Prefect Developers
Match in 5 days, Not Months
You get qualified Prefect developers in 5 days on average. Traditional recruiting firms take 42+ days just to present viable candidates.
Elite 3% Selection Rate
Our vetting process accepts 3 out of every 100 applicants. You interview developers who've already cleared technical and communication bars.
40-60% Cost Reduction
Senior engineers at 40-60% less than US market rates. Same expertise, same output quality, fraction of the investment.
Year-One Retention Hits 97%
Nearly all our placements stay beyond year one. Quality matches mean you're building teams, not constantly backfilling positions.
Work Your Hours
Developers in timezones 0-3 hours from US hours. Real-time collaboration instead of overnight message lag.
What Our Clients Say
"Tecla successfully found candidates for our team and handled the entire process from scheduling to interviews. They were timely, responsive, and always kept communication flowing through email and messaging apps. I was really impressed with Tecla’s follow-up and thoroughness throughout the process."
"I’m very happy with Tecla. Their support has improved our QA process, reduced bug reports by half, and made our onboarding process twice as fast. The team is responsive, cost-effective, and delivers high-quality candidates on time. Tecla has truly become a trusted extension of our internal hiring team."
"Tecla is organized and provides a strong partnership experience. From hiring multiple engineers within weeks to maintaining consistent communication and feedback, they’ve shown real professionalism. Their follow-up and collaboration made the entire staffing process efficient and enjoyable."
What Our Pre-Vetted Prefect Developers Deliver
Hire Prefect Developers in 4 Simple Steps
Tell Us What You Need
Review Pre-Vetted Candidates
Interview Your Top Choices
Hire and Onboard
Our Hiring Models
We offer two approaches depending on whether you need individual contributors or a fully managed team.
True Cost to Hire Prefect Developers: LATAM vs. US
US hiring carries overhead most companies underestimate. Beyond salary, you're covering health insurance, retirement matching, payroll taxes, PTO, administrative expenses, and recruiting fees.
US Full-Time Hiring: Hidden Costs Beyond Salary (Per Professional, Annually)
- Health insurance: $10K-$15K
- Retirement contributions: $9K-$18K (401k matching)
- Payroll taxes: $13K-$17K (FICA, unemployment)
- PTO: $8.5K-$11K (accrued time off)
- Administrative costs: $5K-$8K (HR, payroll processing)
- Recruitment costs: $15K-$25K (agency fees, time-to-hire)
Total hidden costs: $65K-$85K per professional
Add base compensation and you're looking at $230K-$270K total annual investment per professional.
LATAM Hiring Through Tecla (Per Professional, Annually)
All-inclusive rate: $96K-$120K annually
Single transparent rate covering compensation, benefits, payroll taxes, PTO, HR administration, recruiting, vetting, legal compliance, and performance management. Zero hidden costs.
The Real Savings
Hiring nearshore Prefect developers cuts costs without cutting quality. US total: $230K-$270K per developer. Tecla rate: $96K-$120K.
The difference is $110K-$174K saved per developer, representing 48-63% cost reduction. A five-developer team costs $1.15M-$1.35M in the US, $480K-$600K through Tecla.
That's $550K-$870K in annual savings with identical technical capability and real-time collaboration. During the first 90 days, Tecla replaces resources at no additional cost if the fit isn't right.
What is a Prefect Developer?
A Prefect developer specializes in building workflow orchestration systems using Prefect's modern Python framework. They architect data pipelines and automation workflows that power analytics, ML operations, and business-critical data processing at scale.
Prefect developers bridge data engineering and DevOps practices. They don't just write DAGs. They design fault-tolerant orchestration systems, implement observability patterns, and architect deployment strategies that scale from local development to production clusters without workflow rewrites.
They sit at the intersection of Python expertise and distributed systems thinking. Understanding task dependencies, state management, and retry strategies separates them from general backend developers who treat orchestration as simple cron jobs with extra steps.
Companies typically hire Prefect developers when migrating from Airflow, building new data platforms, or modernizing legacy batch processing systems. The role fills the gap between data scientists who write transformation logic and platform engineers who manage infrastructure. Someone who understands both workflow design and production reliability patterns.
H3: Business Impact
When you hire a Prefect developer, your data pipelines stop failing silently and start recovering automatically. Most companies see 70-85% reduction in manual intervention for pipeline failures and 3-5x faster time to resolve data quality issues compared to legacy orchestration tools.
Pipeline Reliability: They implement proper error handling, retries, and alerting patterns. This produces 40-50% reduction in pipeline downtime and faster root cause identification when failures occur.
Development Velocity: They build reusable flow templates and deployment patterns that let data teams ship new pipelines in days instead of weeks. Result is 2-3x faster time from pipeline concept to production deployment.
Operational Efficiency: Their monitoring and logging strategies reduce time spent debugging failed runs. This delivers 50-60% reduction in on-call incidents related to data pipeline failures.
Infrastructure Cost: They spot inefficient task patterns, implement caching strategies, and optimize resource allocation. Systems that maintain the same throughput while reducing compute costs by 30-40%.
Your job description either attracts engineers who've built production vector search systems or people who followed a LangChain tutorial once. Be specific enough to filter for actual Chroma experience and real RAG implementation.
What Role You're Actually Filling
State whether you need RAG pipeline development, vector database optimization, or full-stack AI integration. Include what success looks like: "Reduce answer latency to under 200ms for 95th percentile queries" or "Improve retrieval precision from 0.6 to 0.8+ within 90 days."
Give real context about your current state. Are you migrating from Pinecone? Building your first RAG system? Scaling from 100K to 10M embeddings? Candidates who've solved similar problems will self-select. Those who haven't will skip your posting.
Must-Haves vs Nice-to-Haves
List 3-5 must-haves that truly disqualify candidates: "2+ years production experience with vector databases," "Built RAG systems handling 1M+ queries/month," "Optimized embedding pipelines reducing latency by 50%+." Skip generic requirements like "strong Python skills." Anyone applying already has those.
Separate required from preferred so strong candidates don't rule themselves out. "Experience with Chroma specifically" is preferred. "Experience with any production vector database (Chroma, Pinecone, Weaviate, Milvus)" is required.
Describe your actual stack and workflow instead of buzzwords. "We use FastAPI, deploy on AWS ECS, run async embedding jobs with Celery, and do code review in GitHub. Daily standups at 10am EST, otherwise async communication in Slack" tells candidates exactly what they're walking into.
How to Apply
Tell candidates to send you a specific RAG system they built, the retrieval metrics before/after their optimizations, and the biggest technical challenge they solved. This filters for people who've shipped actual systems versus those who played with notebooks.
Set timeline expectations: "We review applications weekly and schedule technical screens within 5 days. Total process takes 2-3 weeks from application to offer." Reduces candidate anxiety and shows you're organized.
Good interview questions reveal hands-on experience with workflow orchestration, error handling patterns, and production reliability versus surface-level framework knowledge.
What it reveals: Strong answers discuss idempotency patterns, retry strategies with exponential backoff, parameter passing for backfill support, and state handling for partial failures. They should mention specific Prefect features like task retries, flow parameters, and result persistence. Listen for understanding of what happens when tasks fail midstream.
What it reveals: This shows they understand the framework evolution, not just copied code examples. Listen for discussion of server vs serverless execution, deployment patterns, API differences, and migration considerations. Candidates who've actually upgraded production systems will mention specific pain points and migration strategies.
What it reveals: Strong candidates walk through the initial state, specific reliability problems (flaky dependencies, timeout issues, resource contention), solutions implemented (proper retries, circuit breakers, resource limits), and metric improvements. They'll cite numbers: "Reduced pipeline failures from 12% to 2.5% by implementing proper error handling and retry logic." Listen for ownership of reliability outcomes, not just feature delivery.
What it reveals: Real production experience means dealing with failures at 3am. Listen for specifics about debugging approach, how they identified the root cause under pressure, the fix they implemented, and monitoring they added. Strong answers include runbook updates, better alerting, and architectural changes to prevent recurrence.
What it reveals: This tests architectural thinking and understanding of scale. Watch for discussions of incremental processing, partitioning strategies, parallel task execution, resource tuning, and identifying bottlenecks. Strong candidates mention specific approaches (chunking, distributed processing, caching) and acknowledge trade-offs between complexity and performance.
What it reveals: Tests practical problem-solving and understanding of observability patterns. Listen for questions about alert fatigue, proposals for conditional notification logic, integration with existing tools (PagerDuty, Slack), and handling false positives. Strong candidates balance perfect monitoring with pragmatic delivery.
What it reveals: Neither answer is wrong, but reveals their natural orientation. Greenfield builders excel at rapid prototyping and new platform development. Reliability engineers thrive maintaining complex production systems at scale. Strong candidates are honest about what energizes them and what feels like a grind. This prevents hiring someone great who hates the actual work.
Frequently Asked Questions
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Connect with Developers from Latin America in 5 days. Same expertise, full timezone overlap, 50-60% savings.
