Python AI/ML Engineer

Role Overview:As a Senior Python AI/ML Engineer, you will join a high-performing Agile team dedicated to building cutting-edge healthcare applications. You will spearhead the implementation of new Generative AI features while adhering to the highest standards of software engineering, security, and compliance. This role requires an expert blend of advanced AI orchestration, cloud engineering, and robust Python development to deliver enterprise-grade solutions in a regulated environment.Key Responsibilities:AI Engineering & OrchestrationDesign and Scale Generative AI Solutions: Build and deploy scalable enterprise AI systems leveraging Anthropic Claude and advanced GPT-style architectures.Implement Agentic AI Frameworks: Develop sophisticated Agentic AI workflows using the Strands Agent Framework, focusing on automated planning, multi-agent reasoning, and dynamic tool use.Manage Cloud AI Infrastructure: Orchestrate LLM lifecycles, model access, and real-time inference using AWS Bedrock.Software & Cloud EngineeringDevelop High-Quality Code: Write clean, optimized, and production-ready Python code for critical healthcare APIs and microservices.Deploy Containerized Applications: Package applications utilizing Docker and manage container orchestration seamlessly across AWS ECS and AWS EKS.Automate CI/CD Pipelines: Implement robust automation frameworks to streamline continuous integration and rapid cloud deployment.Optimization, Security & ComplianceSecure Healthcare Data: Adhere to strict healthcare compliance, data privacy laws, and cloud security protocols for all AI workloads.Optimize Cloud Spend: Monitor, evaluate, and optimize the cost and performance of high-throughput LLM token usage and computing resources on AWS.Qualifications & SkillsMandatory RequirementsExperience: 10+ years of overall software engineering experience, with 7–10 years focused on hands-on AI/ML and advanced software architecture.Core Tech Stack: Expert proficiency in Python, Generative AI engineering, and AWS cloud ecosystems.Large Language Models: Proven development experience with Anthropic Claude and familiarity with GPT architectures.Agentic AI: Deep understanding of Agentic AI concepts including multi-agent systems, structured planning, and tool integration.Infrastructure & Containers: Strong experience with Docker, AWS ECS, AWS EKS, and AWS Bedrock.EducationRequired Degree: Engineering Degree – BE, ME, BTech, MTech, BSc, or MSc in Computer Science, Information Technology, Engineering, or a highly related technical field.CertificationsTechnical Certifications: Active certifications in multiple technologies are highly desirable.Cloud Certifications: AWS certifications are preferred (e.g., AWS Certified Solutions Architect – Professional, AWS Certified Machine Learning – Specialty, or AWS Certified Data Engineer). [1, 2]Good to HaveData & Retrieval: Experience designing RAG (Retrieval-Augmented Generation) pipelines, embeddings, and vector databases.Operations: Hands-on experience with LLMOps / MLOps frameworks for tracking, versioning, and monitoring models.Analytics: Exposure to analytical dashboards and Python data visualization libraries.Desired TraitsSystem Architecture Mindset: Exceptional problem-solving skills with a proven track record in complex system design.Autonomous Delivery: Highly motivated to work independently and drive results in an onsite delivery model.Elite Communication: Excellent stakeholder collaboration skills to translate complex technical AI concepts into business value.Forward-Thinking: A genuine passion for exploring emerging AI technologies, frameworks, and academic research.