AI Engineer Manager

🕒 August 7

🇦🇷 Argentina – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 32%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Blend360

WebsiteLinkedInAll Job Openings

501 - 1000 employees

🏥 Healthcare

🏨 Hospitality

✈️ Travel

💰 $100M Private Equity Round on 2022-08

Healthcare • Hospitality • Travel

Blend360 is a professional services company specializing in AI, data analytics, and data-driven solutions. They work with Fortune 1000 and large enterprise brands to tackle significant challenges by integrating people and artificial intelligence. Blend360 focuses on several domains including business intelligence, data engineering, data science, MLOps, and data governance. Their industries of expertise encompass financial services, energy, healthcare and life sciences, retail, technology, media & telecom, and travel & hospitality. Blend360 is recognized for their AI and data solutions, having earned accolades such as "AI-Enabling Solution of the Year" and being listed among the "Top Generative AI Service Providers 2024.

📋 Description

• Lead AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution • Design and build robust RAG systems, agentic frameworks, and LLM-powered solutions for production environments • Apply advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts • Lead feasibility assessments across prompting, RAG, fine-tuning, classical ML, and hybrid solutions • Design evaluation frameworks using LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates • Run structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models • Identify and categorize model failures, including hallucinations, retrieval misses, instruction-following errors, and quality regressions • Build scalable inference infrastructure and CI/CD pipelines for AI and ML models • Automate the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement • Design APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability • Mentor junior engineers • Contribute to proposals, solution design, and new business initiatives • Communicate with engineering teams, senior stakeholders, and clients • Help clients make pragmatic decisions about AI system capabilities

🎯 Requirements

• 6+ years of professional experience building and deploying AI, ML, or data-driven/software solutions in production environments • Strong expertise in Python • Solid Git practices • Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns • Practical experience with chunking, embeddings, retrieval, reranking, and evaluation • Strong understanding of prompt engineering, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts • Proven experience designing AI evaluation strategies, including metrics, dataset curation, structured experimentation, and quality gates • Experience with MLOps/LLMOps tools such as MLflow, Weights & Biases, or similar platforms • Solid cloud experience with AWS, Azure, or GCP • Experience with containerization, orchestration, scalable inference, APIs, and microservices • Understanding of event-driven architectures and production-grade engineering practices • Clear communication with engineering teams, senior stakeholders, and clients • Pragmatic approach balancing innovation, reliability, cost, latency, and business value • Advanced English level required for written and verbal communication • Azure experience preferred • Nice to have: Databricks MLOps platform, LLM fine-tuning, agentic GenAI systems, Infrastructure as Code, security and observability practices for AI services, classical machine learning, open-source contributions or public technical work

🏖️ Benefits

• Flexible working options • All the equipment you need to harness your talent (Macbook and accessories) • Every day lunches at headquarters, with vegetarian, vegan, gluten and sugar free options • Gourmet meals every Friday with an on-site chef at headquarters • Snacks and beverages available every day at headquarters • After office events, football, tennis and game nights at headquarters • Football league, pool games, tennis courts, chess championships, game and music nights • AWS certifications, study plans, courses and other certifications • English lessons • Learning from teammates on Tech Tuesdays • Mentoring and development opportunities • Anniversary and birthday gifts