Job description

Company Overview: Join our team at Accrete ( accrete.ai ), a product-focused AI company building intelligence-driven platforms that help organizations understand, assess, and act on complex information at scale using GenAI and agentic approaches. We focus on solving real enterprise problems by combining applied AI, strong engineering, and thoughtful system design to deliver measurable business outcomes across industries. Position Overview: We are seeking a Staff Engineer who is excited about building the next phase of our platform with an agent-first, AI-native approach, grounded in clean code and strong architectural thinking. This role goes beyond traditional backend or data engineering. You will design and build systems where LLMs can reason, plan, and act, working alongside well-structured, deterministic services and workflows. The goal is simple and demanding at the same time: ship systems that work reliably in production, scale with real usage, and solve real customer problems. This is a hands-on role for someone who enjoys building, not just designing on paper. You should be deeply comfortable with Python-based systems, and have strong experience building cloud-native, microservices-driven platforms, including data pipelines and distributed systems. You will work closely with product, ML, and platform teams to deliver enterprise-scale solutions, improve backend performance and reliability, and raise the technical bar through thoughtful design, ownership, and execution across teams. Responsibilities: Technical Leadership & Hands-On Contribution • Design and implement backend services, data integration layers, and distributed systems using Python and modern frameworks. • Build scalable microservices and REST/gRPC APIs for both real-time and batch workloads. • Lead technical design and architecture reviews while remaining deeply hands-on with code. • Create and maintain clear technical documentation, including architecture diagrams, component flows, design docs, and API specs. • Communicate technical decisions clearly to engineers, product partners, and non-technical stakeholders. Agentic Systems & AI-Native Architecture • Design and build agent-first systems where LLMs reason, plan, and act alongside deterministic services. • Implement and evaluate agentic frameworks such as LangGraph, CrewAI, AutoGen, or similar open-source tools. • Apply agentic design patterns including planners, routers, evaluators, memory, tool-use, and feedback loops. • Integrate Model Context Protocols (MCP) or equivalent mechanisms to standardize tool access, context sharing, and orchestration across agents. • Balance probabilistic AI behavior with deterministic workflows to ensure reliability, observability, and production safety. • Collaborate closely with ML teams to move models from experimentation into scalable, production-grade systems. Cloud-Native Development • Build and deploy services on AWS using EC2, Lambda, RDS, S3, DynamoDB, and managed AI services where appropriate. • Design systems using Kubernetes, containers, serverless, and event-driven patterns. • Ensure reliability, observability, security, and cost awareness across cloud workloads. ETL & Data Pipelines • Design data-intensive systems including ingestion pipelines, stream processing, and transformation workflows. • Work with tools such as Apache Kafka, Airflow, Dagster, AWS Step Functions, or equivalent orchestration frameworks. • Design and operate systems using multiple databases such as MongoDB, OpenSearch/Elasticsearch, PostgreSQL, and similar technologies. Code Quality, CI/CD & Operational Excellence • Write high-quality, testable code with strong unit and integration test coverage. • Build and maintain Git-based CI/CD pipelines to support fast, reliable releases. • Own production outcomes by monitoring systems, diagnosing issues, and fixing root causes. Collaboration & Mentorship • Work closely with product managers, ML engineers, platform teams, and stakeholders to align on outcomes. • Mentor engineers, lead code reviews, and raise the engineering bar through example. • Advocate for security-first development, operational discipline, and strong engineering culture. Qualifications: • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. • 8 - 10 years of backend and data engineering experience with a strong focus on microservices and distributed systems. • Hands-on experience with cloud platforms such as AWS (Lambda, S3, EC2, RDS), Azure (Azure Functions, Blob Storage, Virtual Machines), or GCP (Cloud Functions, Cloud Storage, Compute Engine). • Strong hands-on experience with Python and frameworks like Flask, FastAPI, or Django and related libraries/frameworks such as NumPy, Pandas, PyDantic etc. • Strong knowledge of data engineering technologies, including ETL tools (Apache Spark, Apache Airflow, AWS Glue, Google Dataflow), data warehouses (Amazon Redshift, Google BigQuery, Snowflake), and big data platforms (Hadoop, Apache Kafka, Apache HBase). • Familiarity with asynchronous and synchronous integration patterns and frameworks (RESTful APIs, GraphQL, WebSockets). • Strong understanding of system design patterns, caching, rate-limiting, and queuing mechanisms. • Proficiency in backend and server-side technologies such as: • Web frameworks: Flask, Django, FastAPI • Messaging protocols: HTTP, AMQP • Database systems: SQL (PostgreSQL, MySQL, Oracle), NoSQL (MongoDB, Cassandra, DynamoDB, OpenSearch), Graph Databases like MemGraph or Neo4j • Caching systems: Redis, Memcached • Serverless computing: AWS Lambda, Azure Functions • Containerization and orchestration: Docker, Kubernetes • MCP, Claude Agents, Google ADK • Knowledge of microservices architecture and design principles. • Experience with version control systems such as Git and CI/CD pipelines. • Experience in setting up and using automated testing tools and frameworks, such as Selenium, PyTest, or JUnit. • Ability to create and maintain detailed sequence diagrams and architectural documentation. • Proficiency in developing effective strategies to debug issues that arise in production environments. • Ability to work collaboratively with cross-functional teams. • Ability to adapt to new technologies and methodologies quickly. • Strong problem-solving skills and attention to detail. • Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams. Career Progression: At Accrete we value professional growth and development of our employees. As a Staff/Principal Engineer - Application, you will have access to a variety of career advancement opportunities and growth within the organization. We provide ongoing training, mentorship, and opportunities to take on leadership roles within your team and work with leaders across departments. Why Join Us: • Innovative Environment: Be part of a team that's at the forefront of technological innovation, utilizing GenAI and ML to create groundbreaking solutions. • Collaborative Culture: Work in a collaborative environment where your ideas are valued, and you have the opportunity to make a real impact. • We provide a flexible work environment • Professional Growth: We're committed to your professional growth and development, offering opportunities for learning and advancement. • Competitive Compensation: Enjoy a competitive compensation and benefits package, including medical insurance. Join us at Accrete ( accrete.ai ) and help build the next generation of AI-native systems. You’ll work on real problems, with real impact, alongside a team that values thoughtful engineering and ownershi p

Requirements

Must-haves

Technical Leadership & Hands-On Contribution
Design and implement backend services, data integration layers, and distributed systems using Python and modern frameworks.
Build scalable microservices and REST/gRPC APIs for both real-time and batch workloads.
Lead technical design and architecture reviews while remaining deeply hands-on with code.
Create and maintain clear technical documentation, including architecture diagrams, component flows, design docs, and API specs.
Communicate technical decisions clearly to engineers, product partners, and non-technical stakeholders.
Agentic Systems & AI-Native Architecture
Design and build agent-first systems where LLMs reason, plan, and act alongside deterministic services.
Implement and evaluate agentic frameworks such as LangGraph, CrewAI, AutoGen, or similar open-source tools.
Apply agentic design patterns including planners, routers, evaluators, memory, tool-use, and feedback loops.
Integrate Model Context Protocols (MCP) or equivalent mechanisms to standardize tool access, context sharing, and orchestration across agents.
Balance probabilistic AI behavior with deterministic workflows to ensure reliability, observability, and production safety.
Collaborate closely with ML teams to move models from experimentation into scalable, production-grade systems.
Cloud-Native Development
Build and deploy services on AWS using EC2, Lambda, RDS, S3, DynamoDB, and managed AI services where appropriate.
Design systems using Kubernetes, containers, serverless, and event-driven patterns.
Ensure reliability, observability, security, and cost awareness across cloud workloads.
ETL & Data Pipelines
Design data-intensive systems including ingestion pipelines, stream processing, and transformation workflows.
Work with tools such as Apache Kafka, Airflow, Dagster, AWS Step Functions, or equivalent orchestration frameworks.
Design and operate systems using multiple databases such as MongoDB, OpenSearch/Elasticsearch, PostgreSQL, and similar technologies.
Code Quality, CI/CD & Operational Excellence
Write high-quality, testable code with strong unit and integration test coverage.
Build and maintain Git-based CI/CD pipelines to support fast, reliable releases.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonmlrestapiawsdesign systemskubernetesci/cdazuregcpgraphqlsqldocker

Platform: breezy
Effort: medium