AI Staff Engineer- Application Development

Company Overview:

Join our team at Accrete (https://www.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 d…