Backend engineer

There are TWO openings for the Backend Software Engineer

Focus is on Python, FastAPI, building ETL Data pipelines, AWS and containerization
Also this one is a MUST HAVE: experience integrating LLM APIs (Azure OpenAI, OpenAI, Anthropic, Vertex AI, Bedrock) into applications, including prompt engineering, token management, and error handling
Nice to have: Kubernetes and Infrastructure-as-code familiarity (Terraform / Pulumi / CloudFormation)
Backend Engineer

We are seeking a highly skilled and motivated Backend Software Engineer with experience building cloud-native systems to join our team. The ideal candidate will have a strong background in building production platforms, APIs, and services that enable internal teams to operate at scale. This role involves designing, building, and maintaining core backend infrastructure and service layers that power workflows across the organization. You will collaborate with data scientists, internal product teams, and other cross-functional stakeholders to understand their needs and deliver reliable, scalable services that accelerate business outcomes.

Qualifications:

Education: Bachelor's or Master's in Computer Science / Software Engineering / related field, or equivalent professional experience.

Experience: 6+ years of experience in software engineering, platform development, or a related field.
Technical Skills:

- Strong proficiency in Python with experience building production-grade services and libraries
- Experience designing and building RESTful APIs using modern Python frameworks (e.g., FastAPI, Starlette)
- Solid experience with relational databases, ORMs (e.g., SQLAlchemy), and schema migration management (e.g., Alembic)
- Experience building production data systems (ETL / data processing workflows)
- Cloud platform experience (AWS / GCP) including services like S3/GCS, Lambda/Cloud Functions, SQS/Pub-Sub, and managed databases
- Proficiency with containerization technologies (Docker)
- Practical experience developing CI/CD workflows — experience shipping code to production environments regularly
- Strong commitment to automated testing practices (unit, integration, and end-to-end testing)
- Strong software engineering fundamentals: version control, code review, testing, and documentation
- Soft Skills: Excellent problem-solving abilities, strong communication skills, and the ability to work effectively in a team environment. Ability to translate data science team needs into well-designed platform abstractions.

Additional Skills (Preferred):

- Kubernetes experience (EKS, GKE) - deploying, scaling, and troubleshooting containerized workloads
- Infrastructure-as-code familiarity (Terraform / Pulumi / CloudFormation)
- Experience integrating LLM APIs (Azure OpenAI, OpenAI, Anthropic, Vertex AI, Bedrock) into applications, including prompt engineering, token management, and error handling
- Experience with data manipulation and analysis using libraries such as pandas, NumPy, and SciPy

Job Responsibilities:

- API & Service Development: Design, build, and maintain scalable backend services and APIs that enable internal teams to integrate platform capabilities into their workflows.
- Database Design & Management: Design and manage database schemas, write performant queries, and oversee migration strategies to support evolving application requirements.
- Service Architecture: Architect and implement shared service layers - including API gateways, authentication, caching, and data access patterns - that the broader organization can leverage.
- Infrastructure & Reliability: Own cloud infrastructure, containerization, and deployment pipelines, ensuring high availability, observability, and cost efficiency.
- Developer Experience: Build well-documented SDKs, libraries, and tooling that make it easy for internal consumers to use platform services without deep infrastructure knowledge.
- Collaboration: Work closely with data scientists, product teams, and other engineers to understand their needs, gather requirements, and translate them into robust platform features.
- CI/CD & Testing: Develop and maintain CI/CD pipelines, automated test suites, and monitoring to ensure reliable, repeatable deployments of services to production.
- Data Pipeline Support: Build and maintain ETL and data processing pipelines, ensuring data quality and integrity at scale.