Sr Machine Learning Engineer

Responsibilities

• check_circle Own the operational backbone of the AI/ML platform — design and run the CI/CD pipelines, model/agent versioning, automated deployment, rollback, and release management that move systems from experimentation to production reliably and repeatably.
• check_circle Stand up comprehensive observability and monitoring infrastructure — model/agent performance, drift, data quality, latency, cost, and reliability — with alerting and automated remediation where possible.
• check_circle Develop production-scale AI systems, including multi-step agentic workflows and multi-agent orchestration platforms, and operationalize them end to end.
• check_circle Drive complete ownership of the AI/ML lifecycle — implementation, testing, deployment, and continuous operational monitoring — delivering projects on schedule and to specification.
• check_circle Design and implement Responsible AI frameworks — hallucination detection, safety guardrails, evaluation systems, and observability — to ensure model reliability, accuracy, and ethical deployment.
• check_circle Establish evaluation frameworks for Large Language Models and agent-based systems, measuring model quality, task success rates, safety compliance, and operational effectiveness.
• check_circle Champion LLMOps and MLOps best practices — infrastructure-as-code, reproducibility, automated testing, and environment parity — across the platform.
• check_circle Partner strategically with cross-functional stakeholders including product managers, data scientists, application teams, vendors, and partners to align on requirements, iterate on solutions, and deliver successful outcomes.
• check_circle Provide hands-on technical leadership, driving architectural decisions across AI development, LLMOps, quality assurance, and production deployment.
• check_circle Proactively identify and resolve technical blockers that could impact project timelines or deliverables.
• check_circle Communicate technical strategy and progress to executive leadership and key stakeholders with clarity and confidence.
• check_circle Engage directly in development and problem-solving on high-complexity technical challenges to maintain project velocity and quality.
• check_circle Drive innovation through research and experimentation with emerging AI technologies and frameworks, evaluating and integrating new capabilities that advance our platform.

Basic qualifications

• 5+ years of proven expertise in designing, building, and deploying AI/ML solutions at scale, with 1–2 years of production experience in Generative AI technologies.
• Comprehensive MLOps/LLMOps experience with hands-on implementation of CI/CD pipelines, model and agent monitoring, versioning, observability, and lifecycle management in production.
• Production deployment experience on major cloud platforms (AWS, Azure, or GCP) with a demonstrated ability to architect, scale, and operate cloud-native ML solutions.
• Expert-level programming proficiency in Python and AI/ML development ecosystems.
• Strong foundation in machine learning including statistical modeling, supervised and unsupervised learning algorithms.
• Advanced skills in prompt engineering with a deep understanding of optimization techniques and best practices for LLM interactions.
• Versatile ML skillset spanning traditional techniques (classification, regression, clustering) and cutting-edge deep learning approaches.
• Production-grade Generative AI experience deploying and maintaining LLMs and multi-modal models in live environments.
• Exceptional analytical capabilities with a track record of solving complex technical problems and thriving in ambiguous, rapidly-evolving situations.
• Outstanding communication and collaboration skills with the ability to translate complex technical concepts for diverse audiences and drive cross-functional alignment.
• Success partnering across organizational levels from individual contributors to senior leadership, building trust and delivering results.
• Proven ability to influence and lead in matrix organizations where collaboration and relationship-building are essential to achieving outcomes.

Preferred qualifications

• Experience with container orchestration and infrastructure-as-code (Docker, Kubernetes, Terraform) for ML workloads.
• Experience with vector databases and embedding technologies.
• Bachelor's degree in Computer Science, Machine Learning, Mathematical Sciences, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.
• Master's degree or Ph.D in Artificial Intelligence, Machine Learning, Mathematical Sciences, Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.

About the company