Senior AI & Data Engineer

Alamar Biosciences•Fremont, CA

2d•$180,000 - $200,000

About The Position

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 looking for a Senior AI & Data Engineer to join our AI team. This is a hands-on individual contributor role for someone who thinks in systems, builds with AI-native tools, and sees automation as a design discipline, not an afterthought. The right person for this role sits at the intersection of three things: building and maintaining the data pipelines and infrastructure that power our AI platform; designing and shipping AI-enabled automations that remove friction from real workflows; and engineering AI agents and integrations that connect our data assets to decision-makers across the company. You will work closely with the Sr. Manager of AI Engineering and a small high-velocity team to deliver production systems across Alamar, including but not limited to commercial, R&D, and operations. This is a role for someone who builds with AI as a core part of the engineering stack. You should have strong opinions about where AI belongs in a pipeline, when to automate versus when to keep a human in the loop, and how to build systems that are observable, auditable, and easy to iterate on.

Requirements

3–5 years of hands-on experience in data engineering, AI/ML engineering, or a closely related software engineering role
Strong Python programming skills; able to write clean, well-structured, production-grade code
Practical experience building and maintaining data pipelines with modern orchestration tools (dbt, Airflow, Dagster, or equivalent)
Hands-on experience with cloud data platforms (Databricks, Snowflake, BigQuery, or equivalent)
Experience building with LLM APIs (Anthropic, OpenAI, Gemini, or equivalent) including prompt design, tool-calling, and RAG implementation
Demonstrated ability to ship working automations or integrations against real systems, not just prototypes
Solid understanding of REST APIs, data modeling, and the fundamentals of distributed systems
Comfortable with ambiguity: able to scope work, prioritize independently, and ask the right questions without waiting for fully defined requirements
Strong communication skills with non-technical stakeholders; able to translate workflow problems into engineering specs and explain technical tradeoffs in plain language

Nice To Haves

Experience working with agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent)
Familiarity with vector databases and embedding-based retrieval (Pinecone, pgvector, or equivalent)
Background in life sciences, biotech, or scientific software; familiarity with LIMS and/or ELN platforms
Experience integrating CRM and ERP systems as data sources or automation targets
Exposure to governance, audit logging, or compliance requirements for production AI systems
Experience building internal tools or lightweight applications that end users actually adopt and use
Contributions to open-source AI or data engineering projects

Responsibilities

Build and maintain ETL/ELT pipelines ingesting structured, semi-structured, and unstructured data from systems including our CRM, ERP, ELN, and more (both internal and external)
Implement and optimize data workflows on our data lakehouse (Delta tables, workflows, Unity Catalog)
Write clean, testable SQL and Python for data transformation, enrichment, and delivery to downstream consumers of data including BI tools and AI agents
Apply data governance and lineage practices from day one, including tagging, access control, and metadata management
Work with the broader data team to evolve the lakehouse schema, ensure data quality, and reduce pipeline fragility
Contribute to the architectural direction of the data platform as it scales to new sources and use cases
Design and build AI-enabled automations that eliminate manual steps from high-friction workflows across sales, operations, R&D, and more
Identify automation opportunities through direct engagement with end users and translate workflow pain points into executable builds
Build and deploy automations using modern tooling including LLM APIs, MCP integrations, workflow orchestration frameworks, and low-code/no-code layers where appropriate
Instrument automations with observability hooks so usage, failures, and performance are visible from day one
Maintain and iterate on deployed automations based on real usage data, not assumptions utilizing an ROI framework to deliver measurable value to business stakeholders
Design for reuse: build components and patterns that can be composed into future automations rather than one-off tools
Build and maintain AI agents that surface structured data to end users through natural language interfaces, covering use cases in commercial intelligence, R&D discovery, and operational reporting
Implement RAG pipelines, tool-calling integrations, and memory/state patterns that make agents reliable and contextually accurate
Integrate agents with internal systems through APIs, MCPs, and custom connectors as required
Write evaluation frameworks and regression tests to measure agent accuracy, reliability, and drift over time
Contribute to the Alamar AI Hub: shared libraries, governance tooling, versioning standards, and deployment patterns used across the agent portfolio
Collaborate with all AI team members and stakeholders across Alamar on agent requirements, scoping, and delivery timelines
Write production-quality Python across all workstreams: data pipelines, agent logic, automation scripts, and API services
Operate with AI-native development practices: use AI coding assistants, generative tooling, and prompt engineering as standard parts of your workflow, not occasional shortcuts
Contribute to code reviews, documentation, and engineering standards for the AI & Data team
Stay current on the LLM and agent ecosystem; bring new tools and techniques to the team with concrete proposals for where they apply
Debug and resolve production issues across the data and AI stack with appropriate urgency and rigor