Updated 15h ago

AI Engineering Lead at Blend360 · AI Job Signal

Company Description

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.

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

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.