• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc. • Develop scalable inference architectures optimized, with ultra-low latency and high throughput • Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities • Develop logging, alerting, and monitoring solutions to track model development, and reliability • Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency • Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

• Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence • Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives • Establish best practices, standards, and processes for ML infrastructure, deployment, and operations • Own technical decision-making for ML infrastructure architecture and tooling choices • Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Drive the design and implementation of robust model serving infrastructure using platforms like Triton, TorchServe, TensorFlow Serving, and KServe • Define inference architecture strategy optimized for ultra-low latency and high throughput • Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data • Collaborate with research teams to bridge the gap between experimentation and production • Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability

• A Senior ML Engineer is responsible for designing, implementing, and maintaining AI systems across various applications. • They contribute to the organization's AI strategy, work on complex solutions and optimize existing systems to enhance performance. • Responsibilities include mentoring junior ML engineers and collaborating with cross-functional teams. • Develop AI applications and solutions by understanding business needs, collaborating with stakeholders, analyzing data, and implementing AI algorithms. • Design, develop, and maintain robust AI systems, including machine learning models and deep learning networks. • Document and demonstrate solutions with clear technical documentation, diagrams, and code comments. • Contribute to the organization’s AI strategy by researching cutting-edge tools and techniques, participating in educational opportunities, and maintaining professional networks. • Identify and resolve performance and scalability issues in AI applications by improving software and addressing bottlenecks and bugs. • Lead and collaborate with cross-functional teams to define and implement innovative AI solutions, optimizing user interaction and experience. • Conduct code reviews and mentor team members to uphold high coding standards. • Translate business requirements into actionable technical requirements. • Work closely with data engineering and data science teams to implement automated and unit testing. • Improve operations by analyzing systems and recommending procedural changes. • Support engineering goals by delivering project outcomes as needed.

• Design, build, and operate production-grade AI/ML systems that power Asteri’s orchestration platform • Collaborate closely with backend, platform, and frontend engineers to integrate AI capabilities into scalable, reliable product workflows • Deploy and iterate on LLM-based applications in production, continuously evaluating quality, latency, and cost • Own retrieval and agentic systems (e.g., RAG pipelines, workflow agents, policy-driven logic) end-to-end • Define and run rigorous evaluation and testing for AI systems, including offline experiments and production monitoring • Improve model performance and system behavior over time through experimentation, tuning, and system-level optimizations • Implement strong engineering practices for AI development, including testing, CI/CD, versioning, and rollback strategies • Stay current with advances in applied AI and generative models and translate relevant techniques into practical product improvements • Partner with cross-functional teams to understand product requirements and translate them into robust AI solutions