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About us At Kuik, we don’t do magic — we do technology. But technology that truly understands people. Since 2010, we’ve been helping organizations embrace meaningful digital transformation: combining engineering excellence with business impact and long‑term vision . This is not just about models or tools. It’s about building AI solutions that are secure, scalable, and trusted. We work as a team, sharing knowledge, challenges, and ownership. We listen, we collaborate, and we focus on outcomes. For this role, you’ll work closely with one of our strategic partners, contributing to enterprise‑grade AI initiatives. What will you do? As a Senior AI Engineer, you will be responsible for implementing AI and Machine Learning solutions on cloud‑based platforms, taking ideas from proof‑of‑concept to production. You’ll apply strong software engineering principles to AI systems and collaborate with business and IT stakeholders to shape future‑state architectures. Your work will focus on operationalizing AI—from LLMs and SLMs to RAG systems—ensuring solutions are governed, observable, and production‑ready. Your responsibilities will include: Enabling enterprise‑ready adoption of AI by integrating and operationalizing advanced AI models (LLMs, SLMs). Enforcing AI governance by design, implementing data‑handling and compliance policies directly in code. Building Prompt and Agent CI/CD pipelines, including evaluation gates, canary deployments, feature flags, and automated rollback. Managing the model lifecycle, including SLM / LoRA fine‑tuning, lineage, and registry governance. Implementing observability across AI workflows: tracing, latency and cost SLOs, and safety alerts. Building provider abstractions across OpenAI, Gemini, Azure OpenAI, and Vertex AI. Operating and improving RAG pipelines, including index rebuilds, freshness windows, and quality dashboards. Developing APIs and microservices to integrate AI capabilities into internal and external applications. Partnering with business stakeholders to identify, validate, and deliver high‑impact AI use cases. Requirements 6–10 years of experience in software engineering, application architecture, or AI engineering roles. Proven experience delivering AI/ML solutions in production environments. Experience & Background 6–10 years of experience in software engineering, application architecture, or AI engineering roles. Proven experience delivering AI/ML solutions in production environments. Core AI / ML Skills Strong programming skills in Python and experience with PyTorch, TensorFlow, or similar frameworks. Experience with supervised, unsupervised, and reinforcement learning. Solid grounding in NLP: tokenization, embeddings, semantic search, classification, summarization. Hands‑on experience with Generative AI & LLMs, including LangChain, LangGraph, LlamaIndex, OpenAI APIs, MCP, Transformers, and prompt engineering. Practical experience with RAG architectures and vector databases (Pinecone, FAISS Weaviate). Strong understanding of statistics, probability, and model evaluation. Software & Cloud Engineering Experience building scalable services using FastAPI or Flask. Strong understanding of cloud platforms (AWS, GCP, or Azure). Hands‑on experience with Docker, Kubernetes, Git, and CI/CD pipelines. Experience developing cloud‑native applications using services such as AWS Lambda, S3, CloudFront, or equivalents. Familiarity with microservices, SOA, and event‑driven architectures. Experience with Java and Java/J2EE design patterns is a plus. Knowledge of DevOps, automated testing, and build/deployment best practices. AI Governance & Operations Experience enforcing AI governance and compliance (PII, PCI, GDPR). Treat prompts and agents as code: versioning, testing, evaluation, and rollback strategies. Experience operating RAG systems with quality monitoring and drift detection.Understanding of provider abstraction, policy‑based routing, and AI communication protocols. Nice to have Exposure to multimodal AI (text, vision, speech). Data engineering experience (ETL, feature engineering, EDA). Familiarity with MLOps practices (monitoring, retraining, CI/CD for ML). Knowledge of enterprise search, embeddings optimization, or knowledge graphs. Experience working with financial or regulated datasets. Personal attributes Curiosity and passion for solving real‑world problems with AI. Strong collaboration and communication skills with cross‑functional teams. Ownership‑driven mindset with attention to code quality and outcomes. Willingness to learn, improve, and accept guidance from senior peers. Adaptability to fast‑paced, evolving environments. What can you expect from us? Stable contract with a salary aligned to your experience. Flexible working hours throughout the year.
