Purpose of the Role
We are looking for a visionary “AI Solution Architect/Engineer” with 4 to 5 years of experience who thrives in the "Day One" environment. This isn’t a role for someone looking to just maintain existing systems; we need a builder who brings infectious energy and a "get-it-done" attitude to every sprint. In this role, you will be the bridge between complex business challenges and cutting-edge AI implementations. You will design, develop, and deploy end-to-end AI/ML solutions that don't just work in a notebook, but scale in production. If you are naturally curious, constantly learning, and view a difficult technical roadblock as an exciting puzzle rather than a chore, you’ll fit right in.
Key Responsibility Areas
• Architectural Design: Design robust, scalable, and secure AI solution architectures, selecting the right stack (LLMs, Vector DBs, RAG pipelines, or traditional ML) to solve specific business problems.
• End-to-End Implementation: Lead the development of AI services from data ingestion and preprocessing to model deployment and monitoring (MLOps).
• Rapid Prototyping: Bring "Day One" energy to proof-of-concepts, moving quickly from an idea to a working prototype to validate value.
• Cross-Functional Collaboration: Act as the technical translator between stakeholders, product managers, and data scientists to ensure the AI strategy aligns with business goals.
• Performance Optimization: Audit and optimize existing AI models and pipelines for latency, cost-efficiency, and accuracy
Knowledge Generic
4–5 years of hands-on experience in software engineering, with at least 2+ years focused specifically on AI/ML deployments.
Job Context
Partners with manufacturing IT, OT, and process SMEs to deliver AI solutions improving system reliability, integration, and operational efficiency.
Apply AIOps techniques for incident reduction, root‑cause analysis, and operational stability across manufacturing IT systems.
Transform system alerts, logs, and metrics into intelligent AIOps‑driven insights and automations
Job Context Specific
You are ready to take on challenges and pivot quickly when a particular approach doesn't yield the expected results.
You don’t wait for a ticket; you see a gap and you fill it.
Knowledge Specific
Proficiency in Python and frameworks like PyTorch or TensorFlow. Deep experience with cloud platforms (AWS, Azure, or GCP) and AI orchestration tools (LangChain, LlamaIndex, or Haystack).
Solid understanding of data pipelines, SQL/NoSQL databases, and Vector databases (Pinecone, Milvus, Weaviate).
Ability to design distributed systems and microservices architectures that support high-concurrency AI applications.
