Overview
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com We are seeking an AI Engineering Lead to contribute to our next level of growth and expansion.
Full job description
About the company
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com
We are seeking an AI Engineering Lead to contribute to our next level of growth and expansion.
Full job description
What is this position about?
- Lead end-to-end project delivery with clear governance and strong stakeholder communication
- Mentor junior engineers and contribute to proposals and new business initiatives
- Define what AI systems should and should not attempt, and communicate risks and tradeoffs transparently to clients
- Design and build RAG systems, agentic frameworks, and LLM-powered solutions robust enough for production
- Apply advanced prompt engineering techniques, including instruction design, few-shot sets, structured outputs, and tool/agent prompts
- Lead feasibility assessments to select the right approach among prompting, RAG, fine-tuning, or classical ML
- Design evaluation frameworks, including LLM-as-a-judge methods, custom metrics (recall@k, precision@k), and go/no-go gates
- Run structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence rather than intuition
- Identify and categorize model failure modes, including hallucinations, retrieval misses, and instruction-following errors
- Build scalable inference infrastructure and CI/CD pipelines for AI/ML models
- Automate the full MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining
- Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability
Qualifications and requirements
- Expert-level Python, strong Git practices, and experience with ML/LLM versioning
- Solid cloud experience across AWS, Azure, or GCP (Azure preferred), plus containerization and orchestration
- Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
- Proven MLOps/LLMOps track record using tools such as MLflow, Weights & Biases, or similar
- Practical evaluation design skills, including metrics, dataset curation, and structured experimentation
- Experience with event-driven architectures, APIs, and microservices
- Strong communication skills, equally comfortable engaging engineering teams and senior stakeholders
- Preferred: experience with the Databricks MLOps platform, LLM fine-tuning, building agentic GenAI systems, Infrastructure as Code, security and observability for AI services, a classical ML background, and open-source contributions
What about languages?
English: Advanced (required for effective communication with global teams)
How much experience must I have?
6+ years of experience building and deploying AI solutions in production environments, with a strong track record across RAG, agentic systems, and MLOps/LLMOps.
Additional information
Our perks and benefits:
📚 Learning Opportunities:
- Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.
- Access to AI learning paths to stay up to date with the latest technologies.
- Study plans, courses, and additional certifications tailored to your role.
- Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
- English lessons to support your professional communication.
👨🏽💻 Travel opportunities to attend industry conferences and meet clients.
👩🏫 Mentoring and Development:
- Career development plans and mentorship programs to help shape your path.
🎁 Celebrations & Support:
- Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
- Company-provided equipment.
⚖️ Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.
Requirements & qualifications
- Expert-level Python, strong Git practices, and experience with ML/LLM versioning
- Solid cloud experience across AWS, Azure, or GCP (Azure preferred), plus containerization and orchestration
- Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
- Proven MLOps/LLMOps track record using tools such as MLflow, Weights & Biases, or similar
- Practical evaluation design skills, including metrics, dataset curation, and structured experimentation
- Experience with event-driven architectures, APIs, and microservices
- Strong communication skills, equally comfortable engaging engineering teams and senior stakeholders
- Preferred: experience with the Databricks MLOps platform, LLM fine-tuning, building agentic GenAI systems, Infrastructure as Code, security and observability for AI services, a classical ML background, and open-source contributions
What about languages?
English: Advanced (required for effective communication with global teams)
How much experience must I have?
6+ years of experience building and deploying AI solutions in production environments, with a strong track record across RAG, agentic systems, and MLOps/LLMOps.
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