This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for a lead-level AI/GenAI Engineer to design and implement next-generation GenAI solutions for one of our clients. The successful candidate will be responsible for translating business needs into intelligent automation solutions using LLMs, copilots, and AI agents that support both end-user interaction and backend service delivery. - Design and implement GenAI solutions (Copilots, AI chatbots, RAG pipelines, AI agents) to enhance service delivery across Tax, Finance, Cyber, and more - Collaborate with delivery teams to embed AI into business workflows, automate decision logic, and improve user experience - Lead the development of LLM-powered assistants for both internal and client-facing Managed Services - Configure and provision Azure environments, including Azure AI Foundry, Azure OpenAI, Azure Data Factory, Azure Storage, Key Vault, and related services - Ensure alignment with data governance, compliance, and integration standards - Hands-on experience with Microsoft Copilot Studio, Azure OpenAI, and LLM model deployment - Strong Skills with Python development (PySpark) - Strong skills in RAG architecture design, prompt engineering, and agent orchestration - Experience with Databricks, Azure Data Factory, and SQL for data-driven AI solutions - Understanding of AI application architecture and LLM integration into enterprise workflows - Comfortable working in a delivery-focused environment with platform adoption over custom development - Proficient English communication skills (written and verbal) - Excellent communication, leadership, and stakeholder management skills Benefits - A stable, long-term contract with opportunities for career growth - Private health insurance - A remote-friendly culture that promotes work-life balance - Continuous training, mentorship, and learning programs to keep you at the forefront of the industry - Free access to AI training resources and state-of-the-art AI tools to elevate your daily work - A flexible Paid Time Off (PTO) policy as well as paid holiday days - Challenging, world-class software projects for clients in the US and LatAm - Collaboration with some of the most talented software engineers in Latin America and the US, in a diverse work environment Company Description Join Devsu and discover a workplace that values your growth, supports your well-being, and empowers you to make a global impact.

• Design and build GenAI use cases based on client's needs and specific data • Understanding of Agentic AI frameworks and design and when they are appropriate for use • Develop and deploy generative AI models using platforms such as Amazon Kendra, Google Vertex AI, and AWS Bedrock. • Implement large language models (LLMs) and transformer models to create advanced AI applications. • Integrate solutions with cloud-based AI services such as Google Cloud Document AI, Google Cloud Gemini, Google Cloud Gemma, and Google Cloud Generative AI Language Models. • Show your curiosity, motivation, and passion for experimentation and continuous learning to drive AI innovation that is applicable across enterprise client domains.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves owning the entire machine learning lifecycle—from data ingestion and model training to deployment, monitoring, optimization, and retraining in production. This role bridges research and product by operationalizing AI models, ensuring they scale reliably, and building the infrastructure that enables innovation in clinical data processing. - Own the full ML lifecycle, including data ingestion, model training, validation, deployment, monitoring, retraining, and retirement. - Transition AI/ML models from prototypes into scalable, production-ready systems. - Build, deploy, and maintain CI/CD pipelines for ML models, ensuring reproducibility, scalability, and reliability. - Design and implement cloud-based infrastructure (AWS, Azure, or equivalent) for training, inference, and monitoring of AI models. - Automate repetitive ML lifecycle tasks to improve efficiency, consistency, and reliability in retraining and deployment workflows. - Integrate large language models (LLMs), generative AI, and NLP solutions into IMO Health’s Clinical AI products, focusing on unstructured clinical data. - Develop scalable inference pipelines and APIs to deliver AI capabilities to customer-facing solutions. - Apply containerization (Docker, Kubernetes) and Infrastructure-as-Code to manage production environments. - Implement monitoring, alerting, and performance dashboards to ensure model quality, detect drift, and maintain operational SLAs. - Optimize deployed models for latency, throughput, reliability, and cost efficiency. - Participate in system design and architecture discussions, providing expertise in MLOps and AI deployment best practices. - Collaborate in an Agile environment with cross-functional teams, aligning technical solutions with product and business goals. Qualifications - 5+ years of professional experience in software engineering, AI/ML engineering, or related roles. - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience). - Strong coding skills in Python or Java, with experience in software engineering best practices. - Hands-on experience deploying, maintaining, and scaling ML models in production environments. - Proficiency with cloud platforms (AWS or Azure), containerization, and Infrastructure-as-Code. - Experience with MLOps tools and workflows (e.g., MLflow, SageMaker, Kubeflow). - Familiarity with CI/CD pipelines, automation, monitoring, and observability for ML systems. - Working knowledge of NLP concepts (tokenization, embeddings, classification, sequence modeling); healthcare domain exposure is a plus. - Experience fine-tuning and deploying LLMs and generative AI solutions. - Strong problem-solving skills with the ability to design scalable, reliable, and maintainable ML systems. - Excellent communication and collaboration skills in cross-functional, distributed teams. - Self-starter with the ability to work independently and contribute from day one. Requirements - Experience with clinical or healthcare AI applications. - Familiarity with Hugging Face, PyTorch, TensorFlow, or other modern ML frameworks. - Prior exposure to agentic AI and generative AI applications. - AWS Associate-level certification (Machine Learning Engineer or Solutions Architect). Benefits Compensation at IMO Health is determined by job level, role requirements, and each candidate’s experience, skills, and location. The listed base pay represents the target for new hires with individual compensation varying accordingly. These figures exclude potential bonuses or sales incentives, which may also be part of the total compensation package. Our recruiter will provide additional details during the hiring process. IMO Health also offers a comprehensive benefits package. To learn more, please visit IMO Health’s Careers Page .

• Lead and partner with fellow leadership members and teams on technical evaluation and adoption of cutting-edge agentic AI platforms, including Anthropic (Claude), LangChain/LangGraph, AWS Bedrock, and other emerging agent frameworks. • Architect, prototype, and productionize multi-agent AI systems for Agentic SOC use cases, including detection, triage, investigation, and response workflows. • Own the design of core agent architecture components, including planning, execution, tool orchestration, memory, context engineering, and long-running agent workflows. • Lead AI agent evaluation systems, including offline and online evaluation pipelines, golden datasets, synthetic data generation, human- and LLM-based judging, and continuous quality monitoring. • Drive LLM fine-tuning and alignment efforts to improve domain-specific reasoning, accuracy, and reliability for security and observability use cases. • Design scalable LLMOps and AI agent infrastructure, including inference routing, latency optimization, cost control, and production observability for agent systems. • Partner with product, security, and data platform leadership and teams to deliver end-to-end AI agent capabilities from prototype to customer-facing production systems. • Lead and partner on technical direction and mentorship for AI engineers working on agentic AI and LLM systems. • Define and implement best practices for AI safety, reliability, evaluation, and monitoring in production agentic systems. • Operate as a senior technical owner in ambiguous problem spaces—setting technical direction, breaking down complex problems, and driving delivery across teams.