About Aivar Innovations
Aivar is an AI‑first technology partner helping enterprises accelerate AI adoption through production‑grade accelerators and intelligent automation. Our teams combine deep AI, cloud, and domain expertise to deliver scalable, repeatable solutions that drive measurable business impact.
Role Overview
As an Associate AI Architect, you will design and guide the implementation of end‑to‑end AI solutions across Aivar’s flagship accelerators and client engagements. You will work closely with senior architects, engineering leads, and customers to translate complex business problems into production‑ready AI systems spanning LLMs, agentic workflows, and real‑time ML platforms.
You are expected to be hands‑on in architecture, technical design, and code reviews, while enabling engineering teams to deliver robust, secure, and scalable solutions on cloud.
Key Responsibilities
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Architect and design AI/ML solutions using LLMs, traditional ML, and agentic workflows for enterprise use cases (automation, analytics, conversational AI, document intelligence, etc.).
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Define end‑to‑end solution architectures on AWS (and optionally Azure/GCP), covering data pipelines, model training/serving, observability, security, and cost optimization.
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Design and review RAG and agentic AI systems (tool‑calling, multi‑agent orchestration, reasoning workflows) for production workloads.
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Collaborate with product, data, and engineering teams to shape technical approaches, APIs, and integration patterns with existing enterprise systems.
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Guide ML engineers on best practices in MLOps/LLMOps, coding standards, performance, and reliability; perform design and code reviews.
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Partner with customers on architecture workshops, POCs, and technical deep‑dives; articulate trade‑offs and ROI of different AI approaches.
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Contribute reusable patterns, reference architectures, and internal accelerators that can be leveraged across multiple projects.
Core Technical Skills (Must‑Have)
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AI / ML & LLMs
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Strong hands‑on experience building and deploying ML models (classification, NLP, CV or time‑series) in production.
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Deep understanding of LLMs and Generative AI: prompt engineering, fine‑tuning/LoRA, embeddings, RAG pipelines, evaluation techniques, and guardrails.
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Experience with one or more modern LLM/GenAI frameworks (e.g., LangChain, LlamaIndex, HuggingFace ecosystem, LangSmith or similar tooling).
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Architecture & System Design
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Proven experience designing distributed, highly available AI systems on cloud (preferably AWS) including APIs, microservices, queues, and event‑driven patterns.
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Ability to design low‑latency inference architectures using GPU‑backed services, model serving frameworks (e.g., SageMaker, Triton, vLLM, Bedrock, or equivalents), and caching strategies.
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Strong understanding of data architecture for AI: data lakes/warehouses, feature stores, and online/offline data access patterns.
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MLOps / LLMOps
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Hands‑on experience with MLOps tooling: experiment tracking (MLflow or Weights & Biases), model registry, CI/CD for ML, monitoring (latency, drift, quality).
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Practical experience setting up training and inference pipelines using containers (Docker) and orchestrators (Kubernetes/EKS or similar).
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Familiarity with observability stacks (logs, metrics, traces) for ML services and ability to define SLOs/SLIs for AI workloads.
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Cloud & Software Engineering
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Strong programming skills in Python, with clean code, testing, and performance optimization habits; experience with PyTorch and/or TensorFlow.
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Solid knowledge of AWS services relevant to AI (e.g., SageMaker, Bedrock, Lambda, ECS/EKS, S3, RDS/NoSQL, IAM, networking basics).
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Experience building and consuming REST/gRPC APIs and integrating AI services into production applications (backend, data platforms, or frontends).
Nice‑to‑Have Skills
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Exposure to agentic AI patterns at scale: multi‑agent coordination, tool routing, workflow engines, and validation layers.
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Experience with voice or real‑time systems (STT/TTS, WebRTC, streaming) or highly interactive conversational AI.
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Prior work in regulated industries (finance, healthcare, life sciences) and familiarity with governance, compliance, and responsible AI practices.
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Knowledge of other languages/frameworks (e.g., Go/Node/Java), front‑end integration patterns, or data engineering stacks (Spark, Kafka, Airflow) is a plus.
Experience & Background
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8+ years of experience in AI/ML, data science, or software engineering with at least 3–4 years building production AI systems on cloud.
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Prior experience in a lead engineer / senior engineer / associate architect capacity, influencing architecture and mentoring engineers.
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Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
