AI ML Engineer (Senior)

AI ML Engineer (Senior)

We’re looking for an AI/ML Engineer to work at the intersection of machine learning, engineering and the business. You will take AI solutions from prototype to production by working directly with internal stakeholders to understand real-world problems, then building, integrating and deploying intelligent systems that automate and improve how work gets done. This is a highly hands-on role for someone with strong machine learning and data structures fundamentals who has built and shipped agentic AI solutions that automate complex workflows. You will operate close to the problem, move quickly from ambiguity to working software, and own outcomes beyond writing code.

What you’ll do

• Work directly with business and product teams to understand complex problems and translate them into ML and AI-driven solutions.
• Design, build and deploy production-grade machine learning models and GenAI/LLM applications.
• Build agentic AI systems, including multi-step agents, tool use, orchestration and memory, that automate end-to-end business workflows.
• Identify manual, repetitive and exception-heavy workflows and replace them with reliable, observable AI-driven automation.
• Apply strong data structures and algorithms knowledge to build efficient, scalable and well-engineered solutions.
• Build and maintain data pipelines, feature engineering and analytics to support model training, evaluation and monitoring.
• Evaluate and improve model and agent performance using metrics, experimentation, guardrails and human-in-the-loop review.
• Integrate AI solutions with existing platforms, APIs, data sources and enterprise systems.
• Own solutions end-to-end, from discovery and design through development, deployment, adoption and continuous improvement.
• Work across the full engineering lifecycle, including coding, testing, CI/CD, deployment, monitoring and production support.
• Diagnose and resolve issues in live environments, including model drift, latency, cost and reliability problems. Partner with architecture, security, platform and data teams to ensure solutions meet enterprise standards for safety, privacy and governance.
• Develop reusable components, frameworks and patterns so successful AI solutions can scale across teams. Document solutions, share learnings and contribute to a strong AI and engineering culture.

What we’re looking for:

• Machine learning and AI Strong hands-on experience in machine learning: model development, training, evaluation, tuning and deployment.
• Strong understanding of ML fundamentals, such as supervised and unsupervised learning, feature engineering and model validation.
• Proven experience building GenAI and LLM applications, including prompt engineering, RAG, embeddings and fine-tuning.
• Demonstrated experience designing and automating workflows using agentic AI (for example, multi-agent systems, tool calling and orchestration frameworks such as LangChain, LangGraph or similar).
• Experience with data engineering and analytics: pipelines, SQL/NoSQL stores and large-scale data processing.
• Familiarity with MLOps practices: model versioning, monitoring, evaluation and retraining.
• Core engineering Strong software engineering fundamentals with strong command of data structures and algorithms.
• Strong proficiency in Python, plus at least one other language such as Java, TypeScript/JavaScript, Go or C#.
• Experience designing and consuming REST APIs, microservices and distributed systems.
• Experience with cloud technologies, preferably GCP, AWS or Azure. Experience with Git, CI/CD, automated testing and containers and/or Kubernetes.
• Understanding of observability, logging, monitoring and production troubleshooting.
• Forward deployment mindset Comfortable turning loosely defined problems into working solutions.
• Able to move quickly between discovery, prototyping and production engineering.
• Strong stakeholder orientation, and able to explain AI concepts and trade-offs to non-technical audiences.
• Strong problem-solving and debugging capability.
• Pragmatic: knows when to build quickly, when to engineer for scale and when to reuse an existing capability. Takes ownership for measurable outcomes, not just technical deliverables.

Advantageous experience:

• Production agentic AI deployments that replaced or significantly reduced manual workflows Cloud-native application development API and platform integration DevOps / SRE or MLOps Highly regulated or enterprise environments Rapid prototyping / innovation engineering

• What success looks like

• Understand → Build → Deploy → Adopt → Scale Turn a business problem into a viable ML or agentic AI solution. Get a working solution into the hands of users quickly. Take AI systems into production safely, with monitoring, guardrails and measurable accuracy. Demonstrate clear business impact, such as hours saved, errors reduced or cycle time cut through automation. Turn successful experiments into reusable engineering capabilities. Why this role is different You will work where the problem is, not just where the engineering team sits. You’ll combine the depth of an ML engineer with the curiosity of a product thinker and the stakeholder skills of a solutions engineer, on problems where the answer isn’t known upfront and you’re expected to discover, build and deploy it.

• Must-have skills

• machine learningmodel developmentagentic AI

• Good-to-have skills

• cloud native application developmentAPI integrationplatform integration