Senior Backend Engineer – ML Infrastructure

Senior Backend Engineer ML

Why Join Tech9?

At Tech9, we are driven by a clear vision—to empower organizations with AI-centered solutions that make them more adaptable, efficient, and future-ready. As a company at the forefront of innovation, we help our clients build exceptional software that not only meets today’s needs but anticipates tomorrow's challenges. Our approach blends cutting-edge AI technology, top-tier talent acquisition, and expert project management to ensure that businesses can scale effectively and deliver high-quality, world-class software on time and within budget.

Our partnerships speak volumes, with clients like Instructure, Young Living, Imagine Learning, Mars Corp., and many others trusting us to lead the way in software development. We are rapidly growing across our offices in the US, LATAM, and India, and we're creating an environment where talented individuals can thrive, collaborate, and have fun while building transformative solutions.

If you're excited by the opportunity to work in a fast-paced, innovative environment where scaling and building the future of software is key, we’d love to hear from you. Join us as we work together to redefine the world of software development!

About the Client

Our client is on a mission to preserve oral genealogical records across Africa. Over the past six years, they've collected more than 2 million interviews, each detailing names across 15+ generations. These records come from villages through knowledge keepers, and they’re transforming how family lineage is documented and authenticated.

To ensure data integrity and prevent fraud, they are now building out a backend system that supports machine learning models for fraud detection—creating infrastructure that makes it difficult to fabricate interviews.

About the Role

We’re looking for a Senior Backend Engineer who will play a pivotal role in shaping the future of one of the most ambitious genealogical efforts on the planet. This is more than just a backend or MLOps role — it’s an opportunity to bridge the gap between engineering, applied AI, and social impact at scale.

You’ll help lead the charge in deploying and operationalizing machine learning systems that validate and protect millions of oral history records collected across Africa. These systems power fraud detection tools, enforce data authenticity, and pave the way for broader AI adoption across the organization.

You’ll collaborate closely with engineers, data scientists, and leadership to design infrastructure that is robust, scalable, and secure — all while experimenting with new ideas, prototyping creative approaches, and pushing the boundaries of what ML can do in the real world. If you thrive in environments where your ideas matter and your work drives long-term transformation, this is your chance to influence an entire institution toward a smarter, AI-enabled future.

What You'll Do

• Build and maintain robust, scalable backend services (Node.js preferred) that support fraud detection algorithms and ML-powered features.
• Deploy and operationalize machine learning models—designing the architecture, CI/CD pipelines, and monitoring systems.
• Collaborate with ML and data science teammates to fine-tune and adapt existing models.
• Design and maintain APIs that expose ML features securely and efficiently.
• Develop tools that enforce validation through media (e.g., recorded interviews, paper-based family trees, images).
• Contribute creative technical solutions to hard problems involving data authenticity, validation, and pattern detection.
• Support the rollout of a major product update, ensuring stability and smooth handoff before onboarding.

Tech Stack

• Backend: Node.js (primary)
• Data & ML: SQL (existing fraud detection logic), OCR, custom-built ML models
• ML Libraries: PyTorch, TensorFlow, Hugging Face (future stack)
• Infrastructure: Docker, Kubernetes
• Cloud: AWS, GCP, or Azure (flexible)

What You Bring

• 7+ years of backend development experience with scalable systems (Node.js, Python, Go, or Java)
• Hands-on experience deploying and maintaining ML models in production (PyTorch, TensorFlow, or Hugging Face)
• Experience customizing/fine-tuning pre-trained models for specific use cases
• Strong grasp of API design (REST/gRPC) and system architecture
• Comfort with CI/CD, observability, and rollback strategies
• Familiarity with containerization (Docker) and orchestration tools (Kubernetes)
• Ability to work cross-functionally with data scientists, UX, and product leaders
• Creative, curious, and confident in proposing new ideas and approaches

Nice to Have

• Experience with MLOps tools (LangChain, MLFlow, etc)
• Exposure to A/B testing and model performance evaluation in production
• Familiarity with streaming platforms like Kafka or Google Pub/Sub
• Experience working on fraud detection or sensitive data integrity projects

Interview Process

Screening Interview (On-Demand HireVue)

Duration: 15–30 minutes

Format: Online assessment where we will gauge your initial qualifications and experience.

Recruiter Q&A

Duration: 10 minutes

Format: Virtual discussion with our recruiter to address any initial questions and go over the job details.

Round 1: Take-Home Assessment

Duration: 1.5–2 hours

Format: A take-home assignment to evaluate your creativity and technical skills, particularly in ML and ML deployment

Round 2: Technical Interview with Client

Duration: 60 minutes

Format: Live technical interview with the Engineering Lead to assess backend fundamentals, engineering hygiene, and overall problem-solving skills.

Round 3: Deep Dive with Leadership

Duration: 60 minutes

Format: Virtual session with Client and the Group Engineering Manager. This round includes a mix of technical discussion and contextual fit for the mission and work.

Round 4 (if needed): Final Conversations

Duration: TBD

Format: As needed, this may include follow-ups with other stakeholders or deeper dives into specific areas of your background or technical experience.

Total Interview Time Investment: 4–5 hours

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