Senior Manager, AI Engineering

Back to jobs New Senior Manager, AI Engineering Fremont, California Apply At Alamar, we are passionate about enabling our customers to make scientific discoveries that translate into clinical outcomes and benefit patients. Our team is growing quickly as we develop innovative approaches to measure critical protein biomarkers from liquid samples that can enable the earliest possible detection of disease. We believe the next frontier in biology is enabled by measuring proteins at higher sensitivity in highly multiplexed assays at the push of a button, which is something only Alamar can do. As we build our team, we seek collaborative, driven, intellectually curious people committed to solving complex challenges. Our culture rewards accountability and cross functional teamwork because we believe this enables the kind of breakthrough thinking that will accelerate our mission. We are seeking a highly technical, hands-on Senior Manager of AI Engineering to architect and build the company’s next-generation data lakehouse and AI agent platform. This leader will write production code, design core systems, and deliver high-performance infrastructure that powers commercial, R&D, and operational use cases across the enterprise. In addition to building the foundation themselves, this person will hire and mentor a small team of high-potential engineers.   This role is ideal for a senior IC who combines deep technical expertise, strong architectural judgment, and the ability to deliver reliably, on time, and at high velocity with the desire to build a team and deliver cutting-edge tech and tools.   Responsibilities   Data Lakehouse Architecture & Implementation   Architect the enterprise data lakehouse spanning a range of internal and external data sets   Design and build scalable ingestion frameworks for structured, semi-structured, and unstructured data   Develop data sanitization and governance workflows to ensure ongoing data integrity   Implement storage and compute layers (e.g., Delta/Iceberg/Parquet) optimized for analytics, modeling, and AI agent workflows   Develop robust ETL pipelines using modern orchestration tools of choice (e.g., dbt, Airflow, Dagster, Synapse/Fabric Pipelines, Databricks Workflows)   Establish data governance, lineage, privacy, and metadata management standards   Deliver high-throughput, low-latency pipelines supporting model training, inference, and orchestration   AI Agent Platform Engineering   Architect and implement the company’s agent platform, including agent orchestration, tool-calling, multi-agent collaboration, memory/state systems, and workflow automation   Build, deploy, and manage agents of varying complexity (RAG bots, workflow agents, scientific analysis agents, multi-step business process agents). Tools will range from internal infrastructure builds to internal agents used by employees all the way to customer and third-party partner facing applications   Integrate observability, evaluation, and safety frameworks for reliability and performance   Develop the internal AI Hub, enabling centralized management, versioning, monitoring, and governance of all agents   Hands-On Engineering   Write and review high-quality production code across a range of languages and frameworks: Python, Terraform, SQL, etc   Build model training and evaluation pipelines, including fine-tuning, RAG pipelines, embeddings, quantization, and inference optimization   Implement distributed compute, GPU optimization, and scalable model serving environments   DevOps experience and knowledge to appropriately manage our cloud infrastructure   Design APIs, microservices, and core platform components used by teams across the organization   Technical Delivery & Execution   Translate broad goals into clear architecture plans, milestones, and execution roadmaps   Deliver projects on-time or ahead of schedule with high reliability and strong engineering rigor   Coordinate cross-functionally to define requirements, manage dependencies, and resolve execution bottlenecks   Ensure platforms and agents meet performance, cost, and reliability targets   Team Leadership & Development   Recruit, onboard, and mentor a small team of junior and mid-level engineers   Establish engineering standards for code quality, testing, delivery, documentation, and architecture   Create a team culture of continuous improvement, results orientation, and innovation with team norms around acting with urgency, technical excellence, and building scalable and maintainable systems   Qualifications   8–12+ years of hands-on experience building production ML systems, AI platforms, and/or distributed data infrastructure   Demonstrated expertise architecting and implementing enterprise data lakehouses (Databricks, Snowflake, Microsoft Fabric, or equivalent)   Strong proficiency in distributed systems, data engineering frameworks, and cloud-native architectures (Azure/AWS/GCP)   Hands-on experience with AI/ML frameworks such as PyTorch, JAX, HuggingFace, Ray, and Triton/ONNX/TensorRT   Experience designing and deploying agent-based systems, orchestration layers, RAG pipelines, and workflow automation   Strong programming skills in Python and experience building APIs, services, and backend systems   Proven track record delivering complex engineering projects on time, with high reliability   Experience mentoring engineers and contributing to hiring and team development   Experience training or optimizing large language models (LLMs  ≥ 7B parameters)   Experience in scientific, biotech, or laboratory data environments   Familiarity with data governance, compliance, or regulated environments (e.g., HIPAA, FDA, ISO)   Experience with workflow orchestration tools (Temporal, Dagster, Prefect)   The base salary range for this full-time position is $180,000 - $220,000 + bonus + equity + benefits. 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