Senior Machine Learning Developer I

Summary

We are seeking a highly technical, hands-on Machine Learning Developer to build, scale, and optimize next-generation autonomous AI agents and enterprise solutions. In this role, you will be the primary technical driver for developing agentic solutions that serve our internal engineering, operations, and field teams.
While you will contribute to system design and architectural decisions, the majority of your time will be spent writing high-quality production code, tackling complex engineering challenges, and building robust AI features. You will also serve as a technical anchor for the team by actively mentoring junior developers, leading code reviews, and elevating the team’s engineering standards. The ideal candidate has deep expertise in software engineering, extensive knowledge of AI/ML concepts, and a proven ability to transform theoretical AI models into tangible, scalable software products.

Key Responsibilities

• Build AI Agentic Systems: Implement and iterate on autonomous agent workflows and utility agents designed to automate complex enterprise tasks, ensuring high reliability and error handling.
• Hands-on AI Software Development: Write clean, scalable, and production-ready code to build robust AI/ML products, multi-agent workflows, and intelligent backend APIs.
• Mentorship & Technical Guidance: Serve as a technical mentor and role model for junior developers. Lead rigorous code reviews, conduct pair programming sessions, and share knowledge on AI best practices, design patterns, and debugging techniques.
• Cloud-Native Engineering & Deployment: Build and deploy applications on scalable cloud infrastructure. Manage the integration of ML models, vector databases, and traditional data stores within containerized environments.
• RAG & Search Implementation: Code and optimize advanced custom RAG pipelines. Tackle difficult engineering challenges related to data ingestion parsing, chunking strategies, vector search latency, and model hallucination mitigation.
• Cross-functional Collaboration: Partner closely with product owners, data scientists, software developers, and external stakeholder to seamlessly deploy AI agents into production environments while adhering to enterprise security and networking standards 

Critical Qualifications

•  Programming Proficiency: Expert-level proficiency in Python, alongside experience with other modern programming languages (e.g., C#, Java). Building enterprise grade software solutions
•  AI/ML Knowledge: Deep conceptual and practical understanding of generative AI, Large Language Models (LLMs), natural language processing (NLP), vector databases, and agentic orchestration frameworks.
• Cloud Infrastructure Expertise: Proven expertise in architecting and deploying applications on major cloud platforms (GCP preferred). Strong understanding of cloud-native architectures, containerization (Docker, Kubernetes), and CI/CD pipelines.
• Hands-On Development Experience: 5+ years of overall software engineering experience, with a significant portion dedicated to building and deploying AI/ML products or complex software applications
• Systems Design Mindset: Exceptional ability to design complex distributed systems, with a strong grasp of API design, microservices architecture, and enterprise security principles.
• Mentorship Skills: Demonstrated experience guiding and leveling-up junior engineers, leading technical deep-dives, and enforcing high standards for code quality and testing.
• Education: Bachelor’s or Master’s in Computer Science, Engineering, or a related field (or equivalent experience).

Preferred Qualifications

• Deep, practical experience with Google Cloud Platform (GCP) and its AI ecosystem (e.g., Vertex AI, Gemini models, Google Kubernetes Engine).
• Strong understanding of the software development lifecycle and best practices that ensure reliability, security, observability and performance are embedded into the agentic solution
• Experience implementing AI/ML lifecycle management (model monitoring, evaluation frameworks for LLMs, prompt versioning).
• Experience navigating complex enterprise IT environments, including network connectivity and security protocols.
• Familiarity with scrum, agile development frameworks.
• Strong understanding of emerging AI-centric development methodologies and frameworks (e.g. SDD, BMAD etc.)

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